{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gc#垃圾回收机制\n",
    "import time\n",
    "import pickle#数据保存为一个pkl格式\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "from itertools import product\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn import preprocessing \n",
    "from xgboost import plot_importance\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "def plot_features(booster, figsize):    ##绘图函数\n",
    "    fig, ax = plt.subplots(1,1,figsize=figsize)\n",
    "    return plot_importance(booster=booster, ax=ax)\n",
    "\n",
    "def downcast_dtypes(df):#数据类型转换函数\n",
    "    float_cols = [c for c in df if df[c].dtype == \"float64\"]\n",
    "    int_cols = [c for c in df if df[c].dtype in [\"int64\", \"int32\"]]\n",
    "    df[float_cols] = df[float_cols].astype(np.float16)\n",
    "    df[int_cols] = df[int_cols].astype(np.int16)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据的读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"DATA/sales_train_v2.csv\")\n",
    "test = pd.read_csv(\"DATA/test.csv\").set_index(\"ID\")\n",
    "shops = pd.read_csv(\"DATA/shops.csv\")\n",
    "cats = pd.read_csv(\"DATA/item_categories.csv\")\n",
    "items = pd.read_csv(\"DATA/items.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##    <font color =red >针对于train数据</font>\n",
    "\n",
    "\n",
    "##  数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAELCAYAAADa2oIHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAD9lJREFUeJzt3X2MZWV9B/Dvjx1cG6TKW41V00FH01oRxa2RtLXQIq7GBAuampKwliZU2yI28Q8aNkWSJbGvKaykZLXGpWnqG9vUNhFYW2qbvoiLBRZikIFuU6sRXapFkqILT/+4Z+jsujNz73Jnlp3n80lu7rnPOc89z/3tufd+95xz51RrLQAAvTruaA8AAOBoEoYAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdm5lk4VNPPbXNzs6u0lAAAKbnzjvv/FZr7bSVlpsoDM3OzmbPnj1HPioAgDVSVf8xznIOkwEAXROGAICuCUMAQNeEIQCga8IQANA1YQgA6JowBAB0TRgCALomDAEAXROGAICuCUMAQNeEIQCga8IQANA1YQgA6JowBAB0TRgCALomDAEAXROGAICuCUMAQNcmCkMPPvhgtm/fvlpjAQBYcxOFoQMHDmR+fn61xgIAsOYcJgMAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdO6IwtH379mzfvn3aYwEAWHMzR9Jpfn5+2uMAADgqHCYDALomDAEAXROGAICuCUMAQNeEIQCga8IQANA1YQgA6JowBAB0TRgCALomDAEAXROGAICuCUMAQNeEIQCga8IQANA1YQgA6JowBAB0TRgCALomDAEAXROGAICuCUMAQNeEIQCga8IQANA1YQgA6JowBAB0TRgCALomDAEAXROGAICuCUMAQNeEIQCga8IQANA1YQgA6JowBAB0TRgCALomDAEAXROGAICuCUMAQNeEIQCga8IQANC1mSPpdPfddydJzjnnnGmO5airqrTWnnp80UUX5eabb87GjRtzww03ZG5uLkkyPz+fK664IhdffHF27NiRCy+8MLt27crVV1+dc889d9l17N+/P9dcc03e+9735vrrr8/VV1+dU045ZazxLfSdpA8AjKvX7xl7hhZZHISS5Oabb06SPP7449m2bdtT7du2bctjjz2WHTt2JEl27dqVJLn22mtXXMfOnTuzd+/ebNu2LXv37s1NN9009vgW+k7SBwDG1ev3zMRhaGGvUG/27duX+fn5zM/PZ9++fYdd5sCBA7n99tuXfI79+/fnlltuSWst+/btS2stt9xyS/bv37/i+hf3HbcPAIyr5+8Ze4YmsG3btoP2EB3OcnuHdu7cmSeffPKgtieeeGKsBL6477h9AGBcPX/PrBiGquqyqtpTVXvWYkDPZPv27Vtyr9CCAwcOLDnvc5/73A/MP3DgQHbv3r3iuhf3HbcPAIyr5++ZFcNQa21Ha21Ta23TWgzomWx2djazs7PLLjMzs/Q56eedd94PzJ+Zmckb3/jGFde9uO+4fQBgXD1/zzhMNoGtW7dm69atyy5z1VVXLTlvy5YtOe64g0u+YcOGXHLJJSuue3HfcfsAwLh6/p6ZOAydeeaZqzGOZ7zZ2dnMzc1lbm5uyb1DMzMzy/60/pRTTsnmzZtTVZmdnU1VZfPmzWP9fHFx33H7AMC4ev6esWdokao66PFFF12UJNm4ceNBe4S2bt2aE044IZdddlmS5MILL0yy/F6hBVu2bMkZZ5yRrVu35owzzpgoeS/07SmtA7B2ev2eqUP/ts5yTjzxxHbppZc+9fi6665bjTEBADxtVXXnOOc82zMEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrM0fSaW5ubtrjAAA4Ko4oDF1++eXTHgcAwFHhMBkA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0LWJwtDMzEzm5uZWaywAAGuuWmtjL7xp06a2Z8+eVRwOAMB0VNWdrbVNKy3nMBkA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAQBdE4YAgK4JQwBA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAulattfEXrno0yf2rN5xunJrkW0d7EOuAOk6PWk6HOk6HOk5P77X8sdbaaSstNDPhk97fWtt0hANiUFV71PHpU8fpUcvpUMfpUMfpUcvxOEwGAHRNGAIAujZpGNqxKqPojzpOhzpOj1pOhzpOhzpOj1qOYaITqAEA1huHyQCAro0Vhqpqc1XdX1XzVXXlag/qWFFV+6pqb1XdVVV7hraTq2p3VT0w3J80tFdVXT/U8J6qOmvR82wZln+gqrYsan/t8PzzQ99a+1e5Oqrqo1X1cFXdu6ht1Wu31DqOVUvU8QNV9V/DdnlXVb1l0bzfHmpyf1W9aVH7Yd/jVXV6VX1hqNcnqupZQ/vG4fH8MH92bV7x6qiqF1fV7VX15aq6r6quGNptkxNYpo62yQlV1bOr6o6qunuo5TVD+8Svf1o1Xtdaa8vekmxI8mCSlyR5VpK7k7xipX493JLsS3LqIW2/l+TKYfrKJL87TL8lyWeTVJLXJ/nC0H5ykoeG+5OG6ZOGeXckOXvo89kkbz7ar3mKtXtDkrOS3LuWtVtqHcfqbYk6fiDJ+w+z7CuG9+/GJKcP7+sNy73Hk3wyyTuH6RuTvGeY/vUkNw7T70zyiaNdi6dZxxckOWuYPjHJV4Z62SanU0fb5OS1rCTPGaaPT/KFYVub6PVPs8br+TbOnqHXJZlvrT3UWvteko8nuWCMfr26IMnOYXpnkrctar+pjfxrkudV1QuSvCnJ7tbaI621/06yO8nmYd4Pt9b+pY22yJsWPdcxr7X2D0keOaR5LWq31DqOSUvUcSkXJPl4a+3x1tq/J5nP6P192Pf4sOfi55N8euh/6L/JQh0/neQXFvZ0HItaa19vrX1pmH40yZeTvDC2yYksU8el2CaXMGxb3x0eHj/cWiZ//dOs8bo1Thh6YZL/XPT4q1l+4+5JS3JbVd1ZVZcNbc9vrX09GX0wJPmRoX2pOi7X/tXDtK9na1G7pdax3vzmcPjmo4sOu0xax1OSfLu1duCQ9oOea5j/nWH5Y95weOE1Gf1P3DZ5hA6pY2KbnFhVbaiqu5I8nFGwfjCTv/5p1njdGicMHS5Z+wnayE+31s5K8uYkv1FVb1hm2aXqOGl7j9RuMn+S5KVJXp3k60n+cGifZh3XZY2r6jlJbk7yvtba/yy36GHabJODw9TRNnkEWmtPtNZeneRFGe3J+YnDLTbcT6uW666O4xgnDH01yYsXPX5Rkq+tznCOLa21rw33Dyf5y4w21m8Mu8Qz3D88LL5UHZdrf9Fh2teztajdUutYN1pr3xg+RJ9M8uGMtstk8jp+K6PDPzOHtB/0XMP852b8w3XPSFV1fEZf4H/eWts1NNsmJ3S4Otomn57W2reT/H1G5wxN+vqnWeN1a5ww9MUkLxvOLn9WRidmfWZ1h/XMV1UnVNWJC9NJzk9yb0a1WfgFyZYkfzVMfybJJTXy+iTfGXaJ35rk/Ko6adh1fH6SW4d5j1bV64djuJcseq71ai1qt9Q61o2FL9bBL2a0XSaj1/7O4Vcnpyd5WUYn9R72PT6c23J7krcP/Q/9N1mo49uT/N2w/DFp2E7+NMmXW2t/tGiWbXICS9XRNjm5qjqtqp43TP9QkvMyOgdr0tc/zRqvX+OcZZ3RLye+ktHxyqvG6bPebxmdgX/3cLtvoS4ZHW/92yQPDPcnD+2V5IahhnuTbFr0XJdmdFLbfJJfWdS+KaMPjQeTfCjDH8lcD7ckf5HR7vLvZ/Q/lF9di9ottY5j9bZEHf9sqNM9GX0QvmDR8lcNNbk/i36duNR7fNjO7xjq+6kkG4f2Zw+P54f5LznatXiadfyZjA4F3JPkruH2Ftvk1Opom5y8lq9K8m9Dze5N8jtH+vqnVeP1fPMXqAGArvkL1ABA14QhAKBrwhAA0DVhCADomjAEAHRNGAIAuiYMAcuqqn8e7mer6peP9niSpKp+tKo+vfKSACvzd4aAsVTVOUne31p761Eex0z7/4tIAjxt9gwBy6qq7w6TH0zys1V1V1X91nBF7d+vqi8OVyP/tWH5c6rq81X1yar6SlV9sKourqo7qmpvVb10mXV9rKpurKp/HPq+dWh/V1V9qqr+Osltw16qe4d5G6rqD4bnvqeqLh/aXzuM486quvWQS0IAPGVm5UUAkiRXZtGeoaq6LKNrcv1UVW1M8k9Vdduw7JkZXWH7kSQPJflIa+11VXVFksuTvG+Z9cwm+bmMrnJ+e1XNDe1nJ3lVa+2RqppdtPxlSU5P8prW2oGqOnm4WOj2JBe01r5ZVb+U5NqMLpUBcBBhCDhS5yd5VVUtXNDxuRldBPJ7Sb7YRhcnTVU9mGQhJO1Ncu4Kz/vJNrq6+QNV9VCSHx/ad7fWDncV8vOS3Lhw6GwIS69M8soku0fXDs2GjK7hBvADhCHgSFWSy1trtx7UODq36PFFTU8uevxkVv7cOfRExoXHjy0zjkP7VJL7Wmtnr7AuAOcMAWN7NMmJix7fmuQ9wyGpVNXLq+qEKaznHVV13HBu0UsyutL2cm5L8u6qmhnGcfLQ57SqOntoO76qfnIKYwPWIXuGgHHdk+RAVd2d5GNJrsvo/J4v1ehY1DeTvG0K67k/yeeTPD/Ju1tr/zsc6lrKR5K8PMk9VfX9JB9urX1oOHx3fVU9N6PPuj9Oct8UxgesM35aDzxjVNXHkvxNa83fEALWjMNkAEDXHCYD1lxVXZXkHYc0f6q19q6jMBygcw6TAQBdc5gMAOiaMAQAdE0YAgC6JgwBAF0ThgCArv0fmTcpPfoLS2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))#图的大小\n",
    "plt.xlim(-100, 3000)#范围\n",
    "sns.boxplot(x=train.item_cnt_day)#自变量\n",
    "# plt.show()\n",
    "plt.figure(figsize=(10,4))#图的大小\n",
    "plt.xlim(train.item_price.min(), train.item_price.max()*1.1)#最大值乘以1.1\n",
    "sns.boxplot(x=train.item_price)#自变量\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据可视化探索后进行数据的异常值删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除特异值\n",
    "train = train[train.item_price<100000]\n",
    "train = train[train.item_cnt_day<1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "399.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"item_price\"].median()##中值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>22154</td>\n",
       "      <td>999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2554</td>\n",
       "      <td>1709.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2555</td>\n",
       "      <td>1099.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  date_block_num  shop_id  item_id  item_price  item_cnt_day\n",
       "0  02.01.2013               0       59    22154      999.00           1.0\n",
       "1  03.01.2013               0       25     2552      899.00           1.0\n",
       "2  05.01.2013               0       25     2552      899.00          -1.0\n",
       "3  06.01.2013               0       25     2554     1709.05           1.0\n",
       "4  15.01.2013               0       25     2555     1099.00           1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据的填充--因为价格是没有负的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = train[(train.item_price>0)].item_price.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train.item_price<0, 'item_price'] = median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#清除重复行--删掉没必要的减少计算量\n",
    "train.drop_duplicates(subset=[\"date\",\"date_block_num\",\"shop_id\",\"item_id\",\"item_price\",\"item_cnt_day\"],keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  4,  6,  3,  2,  7, 10, 12, 28, 31, 26, 25, 22, 24, 21, 15, 16,\n",
       "       18, 14, 19, 42, 50, 49, 53, 52, 47, 48, 57, 58, 59, 55, 56, 36, 37,\n",
       "       35, 38, 34, 46, 41, 44, 39, 45], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"shop_id\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集中的train和test数据集保持一致性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "##有几家商店是彼此的复制品,改变训练集和测试集将其更改为同一商店编号\n",
    "# Якутск Орджоникидзе, 56\n",
    "train.loc[train.shop_id == 0, 'shop_id'] = 57\n",
    "test.loc[test.shop_id == 0, 'shop_id'] = 57\n",
    "# Якутск ТЦ \"Центральный\"\n",
    "train.loc[train.shop_id == 1, 'shop_id'] = 58\n",
    "test.loc[test.shop_id == 1, 'shop_id'] = 58\n",
    "# Жуковский ул. Чкалова 39м²\n",
    "train.loc[train.shop_id == 10, 'shop_id'] = 11\n",
    "test.loc[test.shop_id == 10, 'shop_id'] = 11\n",
    "\n",
    "#清除训练集中在测试集中不存在的商店并且将数据集重做\n",
    "train = train.merge(test[['shop_id']].drop_duplicates(), how = 'inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=red>针对于shops数据</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shop_name</th>\n",
       "      <th>shop_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!Якутск Орджоникидзе, 56 фран</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!Якутск ТЦ \"Центральный\" фран</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Адыгея ТЦ \"Мега\"</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Балашиха ТРК \"Октябрь-Киномир\"</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Волжский ТЦ \"Волга Молл\"</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        shop_name  shop_id\n",
       "0   !Якутск Орджоникидзе, 56 фран        0\n",
       "1   !Якутск ТЦ \"Центральный\" фран        1\n",
       "2                Адыгея ТЦ \"Мега\"        2\n",
       "3  Балашиха ТРК \"Октябрь-Киномир\"        3\n",
       "4        Волжский ТЦ \"Волга Молл\"        4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shops.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分解出商店所在城市\n",
    "shops.loc[shops.shop_name == 'Сергиев Посад ТЦ \"7Я\"', 'shop_name'] = 'СергиевПосад ТЦ \"7Я\"'\n",
    "shops['city'] = shops['shop_name'].str.split(' ').map(lambda x: x[0])#分开\n",
    "shops.loc[shops.city == '!Якутск', 'city'] = 'Якутск'\n",
    "shops['shop_city'] = LabelEncoder().fit_transform(shops['city'])#直接将其标签编码\n",
    "#分解出商店名称\n",
    "shops['name1'] = shops['shop_name'].apply(lambda x: x.lower()).str.replace('[^\\w\\s]', '').str.replace('\\d+','').str.strip()\n",
    "shops['shop_name1'] = LabelEncoder().fit_transform(shops['name1'])\n",
    "#分解出商店经营类型\n",
    "shops['type'] = shops['name1'].apply(lambda x: 'мтрц' if 'мтрц' in x else 'трц' if 'трц' in x else 'трк' \n",
    "                                               if 'трк' in x else 'тц' if 'тц' in x else 'тк' if 'тк' in x else 'NO_DATA')\n",
    "shops[\"shop_type\"] = shops[\"type\"].map({'NO_DATA': 0 ,'мтрц': 1 ,'тк': 2 ,'трк': 3 ,'трц': 4 ,'тц': 5 })\n",
    "\n",
    "#更新数据shop数据\n",
    "shops = shops[['shop_id','shop_city','shop_name1','shop_type']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shop_id</th>\n",
       "      <th>shop_city</th>\n",
       "      <th>shop_name1</th>\n",
       "      <th>shop_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   shop_id  shop_city  shop_name1  shop_type\n",
       "0        0         29          56          0\n",
       "1        1         29          58          5\n",
       "2        2          0           0          5\n",
       "3        3          1           1          3\n",
       "4        4          2           2          5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shops.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=red>针对于item_category_id数据</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_category_name</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC - Гарнитуры/Наушники</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Аксессуары - PS2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Аксессуары - PS3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Аксессуары - PS4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Аксессуары - PSP</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_category_name  item_category_id\n",
       "0  PC - Гарнитуры/Наушники                 0\n",
       "1         Аксессуары - PS2                 1\n",
       "2         Аксессуары - PS3                 2\n",
       "3         Аксессуары - PS4                 3\n",
       "4         Аксессуары - PSP                 4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分解出商品名\n",
    "cats['split'] = cats['item_category_name'].str.split('-')\n",
    "#分解出商品所在类型\n",
    "cats['type'] = cats['split'].map(lambda x: x[0].strip())\n",
    "cats['item_type'] = LabelEncoder().fit_transform(cats['type'])\n",
    "# # 去空格\n",
    "cats['subtype'] = cats['split'].map(lambda x: x[1].strip() if len(x) > 1 else x[0].strip())\n",
    "cats['item_subtype'] = LabelEncoder().fit_transform(cats['subtype'])\n",
    "#更新数据\n",
    "cats = cats[['item_category_id','item_type','item_subtype']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_category_id</th>\n",
       "      <th>item_type</th>\n",
       "      <th>item_subtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_category_id  item_type  item_subtype\n",
       "0                 0          0            29\n",
       "1                 1          1             9\n",
       "2                 2          1            10\n",
       "3                 3          1            11\n",
       "4                 4          1            13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=red>针对于item数据</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!ABBYY FineReader 12 Professional Edition Full...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>***В ЛУЧАХ СЛАВЫ   (UNV)                    D</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>***ГОЛУБАЯ ВОЛНА  (Univ)                      D</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>***КОРОБКА (СТЕКЛО)                       D</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           item_name  item_id  \\\n",
       "0          ! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D        0   \n",
       "1  !ABBYY FineReader 12 Professional Edition Full...        1   \n",
       "2      ***В ЛУЧАХ СЛАВЫ   (UNV)                    D        2   \n",
       "3    ***ГОЛУБАЯ ВОЛНА  (Univ)                      D        3   \n",
       "4        ***КОРОБКА (СТЕКЛО)                       D        4   \n",
       "\n",
       "   item_category_id  \n",
       "0                40  \n",
       "1                76  \n",
       "2                40  \n",
       "3                40  \n",
       "4                40  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D',\n",
       "       '!ABBYY FineReader 12 Professional Edition Full [PC, Цифровая версия]',\n",
       "       '***В ЛУЧАХ СЛАВЫ   (UNV)                    D', ...,\n",
       "       'Язык запросов 1С:Предприятия 8 (+CD). Хрусталева Е.Ю.',\n",
       "       'Яйцо для Little Inu', 'Яйцо дракона (Игра престолов)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items[\"item_name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分解\n",
    "items['name_s1'], items['name_s2'] = items['item_name'].str.split('[', 1).str##分割成两个\n",
    "items['name_s1'], items['name_s3'] = items['item_name'].str.split('(', 1).str##\n",
    "items['name_s2'] = items['name_s2'].str.replace('[^A-Za-z0-9А-Яа-я]+', ' ').str.lower()#所有大写的均变成小写\n",
    "items['name_s3'] = items['name_s3'].str.replace('[^A-Za-z0-9А-Яа-я]+', ' ').str.lower()\n",
    "items = items.fillna('0')#空值用“0”来填充\n",
    "items['name_1'] = LabelEncoder().fit_transform(items['name_s1'])\n",
    "items['name_2'] = LabelEncoder().fit_transform(items['name_s2'])\n",
    "items['name_3'] = LabelEncoder().fit_transform(items['name_s3'])\n",
    "#更新数据items\n",
    "items = items[['item_id','item_category_id','name_1','name_2','name_3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_category_id</th>\n",
       "      <th>name_1</th>\n",
       "      <th>name_2</th>\n",
       "      <th>name_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  item_category_id  name_1  name_2  name_3\n",
       "0        0                40       0       4    1331\n",
       "1        1                76       1      64      42\n",
       "2        2                40       2       4    1011\n",
       "3        3                40       3       4    1010\n",
       "4        4                40       4       4    1572"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=red >特征重做--特征添加</font>--目的就是做出item_cnt_month"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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ABERABERABERABERABESgAAQk1BUAuqIUAREQAREQAREQAREQAREQgbgISKiLi6TCEQEREAEREAEREAEREAEREIECEJBQlwT6t99+a1544QWzZs2aCr8uWrTInHLKKaZGjRqJv6uuusr88ccf1t/PP/9sWrZsac455xzz008/VXhWX0QgVwSoq7vssosZPHiw8TwvV9EkDVd1PimWjG8Wmmch61LG0Ir4wULyLHRdKuJiUdJySEB1PodwyyBo9Uu5LUQJdQG+CGNPPfWUadq0qVl33XUDvxrTqFEjs3z5cjt4ZgDdv39/s9FGG1XwR6f21VdfVbinLyKQSwK///67GTNmjFmxYkUuo0kZtup8SjQZ/VBInoWuSxkBK+KHCs2zkHWpiItFScshAdX5HMItk6DVL+WmIGvmJtjMQl25cqWZNGmSmTlzptlyyy3NGWeckVlAGT7FituDDz5oLrzwQrPZZptFDmWbbbYxjz76qBXyNtxww0jPFzrvkRIb8Pzbb7+ZyZMnm4kTJ5rjjjvONGzYMOCj4leXV5776KOPzCabbGJuuOGGip5y8M3FO27cuFDpzEESchJkixYtzKabbmpGjRqVdCIiJ5H+f6DZ1Plk6XJlhIDauHFjc+SRRybzVvL3XD6DfV3cPKOCKmRdiprWYvKPdsesWbPMxx9/bNDe4P2FKyTPQtelYiofpSVzAq6vCtsnl1Odj5r3zClXnyfVL+W2rEOv1DH4OOiggxJqh34VxA8++CCWVK5atcqMHz/ePPDAA4YBf77dm2++abbbbjuzzz77ZBz1vHnzzA477GD222+/SGEUOu+REhvwzKzc6NGjTZ8+fUKtFJHXzz77zHz66af2j+fz4Rzjp59+OlQ685GmOOJYvXq1mTp1qjnrrLPM+uuvH0eQkcLItM4ni8SVEaqkQfXnZP6L5R71+YADDjC9evUKlW6Xz2R9XZw8o/LJpC5FzXvUNOXb/59//mk6duxojjnmmNAaFz/++KMZNGiQeffdd83atWsTSc6EZ+LhGD5ErUuZ5D2GZCqIIibg+qqwfXKp1fl06KPmPV1Yxfhbodp71H6pGNkVa5pCr9TVq1fPDsSdyiEDcgbxG2+8cWx5Y7XhuuuuM0uXLo0tzLABLViwwAwbNsx07tzZCq5hn/P7Q/AdMmSIufHGGyupZPr9JfscZ95RwXv22WfN+eefHzkdydJW1T0E4WuuucZ8+OGHVXm1v5NXZrNxpPObb74J9Vy2noj3yiuvNF988UW2QRXN87x0HnvsMXPooYeao48+Ou/pyqbOJ0tsqZYRexrbt29vDj/88FCrpanae9w8kzFOdS/TuhQ27/nul1Lls6r76623nt07ve+++5odd9yxKu/2d1aUWZ277bbbEv4z5ZkIIMsPmdSlKHlHy4LxwFFHHZVlSkvv8XLLe7q2GaVPLsU6n672Rcl7unCK9bdCtPdM+qVi5VeM6Qot1LnEs0KHaiL7zfhcLo5OGuGEAUqmDsGXGd5Cu2XLlhlmjuXKnwCdMgJ1oVyx1PlC5d/FW7t2bXP55Ze7rxlfC8kz07oUNu+l0i/xXjv22GPtX8YFaYzJlGc2cfqfzaQuRcn7119/bXbddVd/lNXmc7nlPa62WYp1vtpU2iQZLUR7z6RfSpJ03UpBILT6ZYrny+I2S9DoizPbmsw4SillElXGhx56yHz//fellOxqk9ZffvnFPP744wmLqS7jGNZhT5xz1MkffvjBfdW1yAmgcvTdd98VRG280GjC5D3OfonVISyo5VJt+9dffzVz5swpNNqCxF9V3uHP/um+ffvGkr5S6uvKMe9xts1YKoQCySuBfLf3vGauGkYWq1DHbA/WIFGtwxgFq1+nn366XdnDzP+XX35ZCfHixYtNp06d7IzfgQceaM2y08knczx/8cUX2/DwS1zuOAH8B48cuP32263RFbcXkCtLv0FHp8YAOt+zjmHzzgDm7rvvtnv9mFlhzx8GXfx55zPcURfo1q2bNdiCaiz+OYYBNkFXFc+g/7DfUc0988wzbTlRXtmoV/ISZb/eSSedZPOC4M3eFTYwBx33+A0/5BujLTxLGKlcsM6gDhrFRanz1EdWNTCm4Jwrt7322sugAoxjj+oGG2xg6zd7tNijeccdd5hp06ZZrtTTF1980QVRUld/nWfFv3nz5lZlLVm7JGP+sqdOVdWHbLXVVlbF1j8gnz9/vundu7dtH7BFRZr+A78dOnQw/J6po76QD2Yf6evYK5DK+fNeVV+XKoxiuh8m765+x9UvEd7VV19tTj75ZMMeaPoFVF7pczBSRdu46KKLrNAXlZXrC9DW2H333c0zzzyTMgh/v0T5X3bZZWbu3Lkp/Rf7D2HzTvtZZ511LHP2v7PvkL6Wv2R9J33ygAEDbHuDE+8D9v86l+u+jn4AlXvaOn9o0tAOnWOMQr/MGATL1/fee6+tQ7xjqU9MWDgXNe/uuVTXTPLu70OS9XVR3keZtE3yEqZPTpXnYrvv51kO7yP4VlXn8RN3e+f9HRxnu/rl+gfamVzuCcQq1LH0zuCGSjVy5Ehr9ISOHqHk4IMPNv369TN+gQ1hqkuXLgZrOOxz4iWBe+ONNyrlnN8RVtq2bWuWLFlixo4da+jAeN4N8OnkXn31VSvwXHrppdaiIukZPny4Xb1iMM33oKOCs4+PgXS+XNi8w+7vf/+7fSFhGZTBBAIzL8bnnnsukVyOVUDI5XfYtGnTJnH0wuuvv26fT3g2xvKuiqfff9jPdPikjQERHWaTJk3MLbfcktEgiziff/5507NnT/uyxQDB0KFDzTvvvGMH6f4XLnXgnnvusb/hB7/MJFM/sMyZylFnGPDfdNNNNr0XXHBBKq9J70ep8+zXxEiL31FurKzefPPNidscm9G9e3ebF47WmDBhgi1TmPI87eitt96qINQnHi7iD3Ty7Dk64ogjrAEK2vGtt95q87Jw4cJKKWflcvr06VYIY8/H8ccfb1m59s4DtA/q+hZbbGE+//xzOzBDuKL9u72TWFdlkI5BD+oTR5LQ1yCAsW+K9pXpuZLUF/oOBofJ+haXqbDt3fkvhWuYvMfdLxEeexdr1qxpBfzXXnvNlvX1119vy5Iypg/EEmVUR19AX4lwhgGbdO69996z7xkm16jHCAX/+c9/bN1K91yx/hY27/RN8KUN0e541/Cdv2DfSf/MvnveA/iDE+9v+kE3sZXLvm7KlCk2TfQHtG+0V3jH855w45Dtt9/e2gWgvlDmJ554op2EZNKMCUL/uyNK3sOUc9S8h+nroryPorZN8hSmTw6T92LwU47vozB1HvZxt3fefWi7MaFGn8h36hdjMN75pIt2L5d7ArEKdXSYrJIwW7r33nubdu3a2Vls7jMjz2qY36olwhvfGYBRAfg799xzzamnnloh5wyI6HAJD4MQzBQyq9K6dWvDYJAVDOd42XMkAS8dZsJ4sbz99tvWMiC/JXNY2fNbLEvmJ+57YfPOgJG8MCMKHxwb8jH3nm5VIF16o/BMF06y3xh883JnIA1vXqjwZbAV1VFfGITzEj7kkEPsbDAv4a5du5r333+/QpgMGj755BMrDOGH2SFWxXDpZtB52SP00+E4M+RR0hm1zidT74UTKxlBxyoEVlQpd3jCtVatWlZAof6XmmMlEl4MnMgz7RgBD6HarVL687Tnnnva/YLklecQcFlVYzIHx0Dy5Zdftn0Ng3pX55o1a2aNBD3xxBN2wgdmrOwwk8iqDhxhSpgYE6pTp44VLP1xR/1MXvhL5cK291TPF/P9qvIeNu1R+iXaKkfeUIbUD/pHBskI9+mE67BpSdZO3bOoUDMRw2QA7ZP8M2mAgayoR9m4MIvpmi7vUdKJJVD2diPIUUZw4v2NMMiEpBOsCDPuvs7VJVZ16Q/ob2jzvBPQHGFAj6tbt64ds/COpQ9x44v999/fvmNnzJgRJcsZ+Q2T97B9He0hyhgsaoKr6pOjhldI/+X2Pgpb54PM42rv9L28o/3aNLyraXNx9MnBdOt7cgKpRyHJ/Ye+u9tuu9mBtXuAQbZ/0EOHzkAci33+ASr+gpWMGTaEvz322MMFZ688R6ccPOibFz4vkn/9619WTQ0BEyMoxeKi5J08MxvNigKORsIKhL/hRM1XVJ5RwqdR8wJ1joaOqpmbmXX3w1wR0rbddluDaqLfoY7IiwvBDofQiOCOoOsv55122smuGAcnCVxYzHwyOGvVqpUVltz9TK9V1fmo4dIOaA84jirghV3KjnYJc1Zz/ausCOzJyqgqnkx4MHPMs/46BzNUb2kjQdXKYJgM9NAiYJUvV0coRGnvpVy+2aY9Sr/Eu8S1DeKlHIPvjWzTk+p5hALeRxxh4Xekx/+O8/9W3T4jhKBRgpAU7LdogwyoGYQ6F3dfhyETBEoM3vjrCaqYTz75pNl8881d1PbKu8J/j2fyVZ/C5D2Ovi6O+hnsP+MIs0JB5PFLub2Potb5XKCmvbMq59o272AmvApx1FIu8lcKYeZMqKsq8wygmB1jVaUqx2ocZ6RsvfXWtoOmI3GdCSqE/gGiC4sVgAYNGtjVOipaOke4rJT41brS+c/2tyh5Jy4aCCp3CDKsOCCI0IAzdZnwDBtX8EVIOXEvWRlVFSaqL5gUZ8Dmd4TJywU9bmZcGTSj4hMU+vHHKo1/wO/C4SXJChETC9mwdOHpWjUBJmHQq0e1iRlfDpx3q+zBgV/VoRkrsLHJm/YbdNyjPSMoVOVo+9Qf6lEuXNT2nos0lEKYueyX4sw/2gj0K6wMyyUnACPaHloz9MP+PyYoKetcascgdFNGTCr6HYPLZFoRfj/F+JnJqTj6umLMW6HSVG7vo2Ko82jpMV5jTMXEDltxgpNfhSrv6hJvwYQ6BzjY6br7wSsrQLwIqCjBv6AuP89yXgqCBOoTVKx0js6fFT+/cYV0/uP6LUzeUQ9ET5lOHTVBZj8ffvhha/wkm3RE5ZlNXPl6NgxPlxZma1HnRe0SvW9WkORyT2DnnXc2Tz31lF1lpc2h/siqOkJVubso9bPcWaTKX6n0S5Ql+5fk0hMYOHBgpfc17+9k+7zThxT9V5VRdGbV7Ylyex8Vus4zOcsWBzSn2EMbduGmutW7XOa3YEIdM3es4GAGvCqH0IVze2mq8s9LA4Mp7Of6xz/+YdUt3HJwsmeZvWNPBiqCuVLB8scbJe/kA/VDhA/2/sThovLMJk54spqWyewoarPsX0m2goL6DkKBU7vC0mcqoZx6E1wpvPbaa61qJ2p6rPAhaOSj7JOtGmbDtxSfpf7zMr3uuuusaXRUn4IGZMLki/bAakmyfoF77G9Cfbcqx55L1HZztR8qSnuvKq3l/Hs++6VsOPLeYhXK7cvKJqxyfZZ3Kntpku2VzUeeacuoXyZ77/MuSNZn5CNdmcYRV1+Xafzl/Fy5vI+Kpc43bNjQalFhPEyql/lvOQUT6qiA7KVB/9Y/4GYQj5VHv2NJlxkI9lgF3YcffljpmAJW5xigEz6DdnSnX3nlleCjFb4fddRRdm9eOqMaFR7I4kuUvGMMBYEolTCACktVaqP8PmLEiESKo/JMPBjiAzMzfoeaI3tQMGIQ1THjM3v27ErntVFHsF6IZU2cE8qZHSI+v+OljjXMoMDm1EThisEdrEnSCeXaIcCQFiYenCPNwX2h7rc4r6xGtmzZ0nC8SKbWHrNJz7ffflvJuA8zexi8wAJtMuE9XXz0CUzGYJDBz5Nn6CsQ+hEe/S5YPxl0UpeCe2/8z2T7OUp7jxLXCy+8YDC/j2p6MP9RwimU33z2S3HmkXpFmQbfFezLpG/KxBW6bWaS5uAzqFK742voX+nzeT8HmdAnU3eDfXIwvGy+M5hkwi84liBM2ns2e9KTpcuf92S/Z3svk74umziDbTObsFI9W+g6X27vo2Kp84xxmITA8n22qpel/o5LVfdzeT+yUMfggUFocGDqEomAhupjsMPmOfH26s0AACAASURBVGY2/bObWKWiYXFuFM/R2WMVi1UXDmhG3RCH7jNWrO6//357NhF++eOcIs4n8u+loqPAFPEJJ5xg9fgZtJ933nl2Jcbt33Fp9V/Z23faaafZwX0+Bkhh805DxRiEM9HNC5L8DRs2zLJEdzn44qJRMVjlN4Q+9o35V8qi8PQzquozAx3KjxemqyeYtObIivr16yd9nHqSbCUNzwifLVq0sGeJuTCZfe3Ro4c1boHQ7hzlTVic6+YGEVzZN8ceDrdni3Thzy9AEM/ll19uj07IRNiJUufZ64WhGwY7lA1tiVVC9OE5soA679JIOvmMP/eZ/CKYkDd/HhyHMFc6ynwIkcG0sLrBuXDUZZjhyD/poYwQznFheTLDiuo1Vm5Rp6Vvcf0Cli85t9GVuw3YGNuvMKiDKX0Fx2Wwb5P9qtk4wmMfUVBodGGGbe/Of9grfSampIm7UK6qvLt0xdUvufbBFUfbcO2D8qc/oV5l4wgvVb/Eqi79En0LdZp0cBg3mh70u/fdd18lAz1h01KotulPX7q8O38ITFi042gHBAD6Iz77V8YxXMXgjv6afhvHFUvWDPYQ/FxZuvKLq69jIhcDWBwNw5YF6gV/fMZQE1ZLnaMfdfG7ey5d1CNXz9xvYfLu/Ka7ujhc3OnyHqWvI59hx2AufVW1zUzCdGFXdS1UnS+391GUOu8vk7jauwuTds0kKW0/jM0M91yqazG841KlrSjveyHdjBkzvAYNGvAWrfQ3duxYG8rChQu9pk2bJn4fOHCgvc/v7jnCICzn5s6d61166aVerVq1vObNm3uTJk3y7r//fq93797e9OnTnTd7nTJliteqVSvrt27dutbP8uXLE36Iz8XTpk0bj9+C6XZpTTzk+7Bs2TLv1ltvrRSv80L+unfvbsN197K5hsn7qlWrvAEDBnj169e3eWvWrJk3ZswYb/bs2V7jxo29Fi1aePPnz6+QjLVr13pDhw61z8Cpf//+HuEEXVU8g/7TfYcN6Vy8eLHXtWtXj3jr1avn9enTpxKvYJm4MuMaLJ81a9Z4o0aN8po0aWLzf8QRR3iDBg1Kmp9FixZ5d911l42XsGA1btw4Dx64VPXTn55g/UyX53Rhpqrz/rKhzlOfZ82aZevyU089Zeuevx4TDnWO/NC2Zs6cmWhj3I/qlixZ4l1yySXexx9/HPXRrP2T9g4dOng33XRTooyC5ZmqjFLxJFELFizwOnbsaOtc7dq1vdatW3u0Lb+jL6BPoC7Rt1A/+evVq5dHu8/U+cvKX4+TlU2Y9h41HSNGjPDat2/vrVy5MuqjWfuPknci89f9TPslf/2gPHlfuPcS6XFpitqOSZ8/bH9ZJguL/pR+iH6ZvFCnJk+e7F177bXeyJEjvaVLl0bmW8i2GSXvLmO0O9qae3fTjwUd7a5fv36J95d7xzt/rrzgHXdfR33jXfm3v/0tMWa45557KpSNP376Vzi4vsLVgWRtOUzeXR5TXf1xh817VX1dsByJA5eu/+T3dG0z0zBT5dt/v5B1vhzfR2HqPPyDZerqerK+zpVXlDpPX0g/GIcr5DsujvTnO4waRFiU0maBEsVKDbP+GG8IWjdDReyRRx6xqw3M1smJQKkRYJUQ4wUcllyd6jCreBxajdGhTFSBi7GcmT1nZZr8lEueipFzvtJUXdtmvvgqnuIjUF3rfDm+j/y166WXXrLHmaCqno3TOy46vcjql9GjKK0nUB8566yzDIcFJ3OoeqEeijoEf6h4+VVKkz2jeyJQDARQe0S9k0OSq5NAB3tUrFCny1RltRjKz58G1KtQUWffKWd9ypU2gercNku75JT6TAlU5zpfbu8j6gD72LFdgfFDtgOg0puN0zsuQ3r5XhpUfKVBwK+S6JbmU11R7SgnV53zXk7l6PISLM9k6lT4DfpLVd+5X2513rEqpyvlnK4M3W9OVb+c8l5ueSmVtlkq6Sy3+lFK+QnWkXJ5Hz3yyCNenTp1vLZt23pshZErDAGpX2YoDOsxERABERABERABERABERABESgGAlK/LIZSUBpEQAREQAREQAREQAREQAREIEMCEuoyBKfHREAEREAEREAEREAEREAERKAYCEioK4ZSUBpEQAREQAREQAREQAREQAREIEMCEuoyBKfHREAEREAEREAEREAEREAERKAYCEioi7EUfv75Z9OyZUtzzjnnGM67kyttAi+88ILZZZddzODBg02+j3NUXSr+ulPoMipk/Sz+0lEKS4WA2lHxl5TKSGVU/ASUQghIqMtBPWCw9dVXX+UgZAWZbwK///67GTNmjD13Jd9xE5/qUiGoR4uzkGVU6PoZjZR8i0BqAmpHqdkUyy8qo2IpidTpKGQZpU6VfskXgZr5iqg6xLPNNtuYRx991B7svOGGG2ad5ZUrV5pJkyZZoaJx48bmyCOPzDpMBRCeQIsWLcymm25qRo0aZdZdd93wD8bgU3UpBog5DiLuMoqa3ELWz6hplX8RcO+zmTNnmi233NKcccYZForaUfHXDZWRyqj4CSiFENBKXcz1YN68eWaHHXYw++23X9Yhr1q1yowfP96q/61Zsybr8BRARQJr1641r732mpk4cWLFH/7/2+rVq83UqVPNWWedZdZff/2kfnJ5U3Upl3TjCTvOMoqaokzq52effWYOOOAA06tXL6M+JSpx+fcTiFqX3PvsgQceML/99ps/KFNq7ahC4svkCyqWTEr/8ccfSXOkMkqKJS8358yZY66//nqz1VZbmc0228w0b97cvPvuu5W2hRSyjPICQpFUSUBCXZWIwntgBnLIkCHmxhtvtKt14Z9M7pNVoiuvvFIrdMnxZHz3k08+MU2bNjWHHnqoadasmfnzzz8rhcUAhBccfo4++uhKv+f6hurSfwl/9NFHZty4cbnGnVH4cZdRlERkWj/ZI9q+fXtz8sknp119XrFihXniiSdSDvCipLXU/FbnvEcpq7B1yYXJ++y6666zk2TuHtdSbEf+9JfyZ+p6586dzYknnmj22WcfqxmULD8qo2RU8nOP8cqtt95qrr76aoPgjb2GU0891bRu3dp8+OGHiUQUsowSidCHghOo4eXbAkTBs1xaCWDWjEHYRRddZBo1alRaiS/S1CLEMVPM7Nfll19uHn744WrBthTr0rPPPmt23XXXalE+xdRcFi5caPr37x/bBFUx5a2qtFTnvFfFJo7fb7/9drP77rubCy64II7gFEYWBBj+LV261E5s9uvXz66YspK60UYbZRGqHo2LANoUCN37779/hfaCKvPNN99sNtlkE0N7yvf2kLjyp3DiJ6CVuviZKsSYCCB8/fDDDzGF9r9gUKVEjQFhYbvttvvfD/pUNAQYbKAW27dv36JJUzkkBJXN7777rpL6mz9vGF956KGHzPfff++/ndFnypHZZcIsBRdn3kshv9mkMUxdyib86vBsrt5xYdnVqFHDbL755oY9c3vssUfYx6qVv0KWEXEvXrzYoLHiV4vdYIMNzNZbb21/w4+cCDgCEuociQJcaaydOnWywoXTk77tttusOkqy5IwePdqcdNJJho74zDPPNF9++WUlb/4wEVxQ32RFyrn58+eb3r17m6uuusp88MEHVl30wAMPtEJOhw4dDL9HdcuWLbOz+oSJqhwd0Omnn251vzneIZhO9rK98847Vi+cfPOHP/ZoOEfa6LhYLWD/D/sU77jjDjNt2jSbdwSyF1980Xm3V2avBgwYYMgPYV588cV2T1wFT2X6xV/upVyXKB7Kfp111jGHH3643VN6zDHH2DpPvWflzjnUTQ466CD7G1e+8+KjHuKXP2YxM6nz5VaX4Ea9qFevnm2b7L0IOscONblu3bpZ9eONN97YcjzllFPMokWLgo/Ytk07I2zaHe3VDT64ojKEquebb75p+y7K9NNPP7Vh04bRQEDoy8RRRoMGDbLq6ZT1cccdZ+gjg8onhH/33Xdb9TL8oWb24IMPJtJJ3HHn3Z8fhOjLLrvMMiLP9PmotsKLq3Ok29/HYxiL/JFP5+hL77nnHnPNNdcYyoQ6j9ZBly5dbB/uP06Huu9vB4RB23L3XJtxYYe9hqlLLix/v0R+OR6mFAehese5Ei3eazmWESum9JHHHnusWW+99RLw6StYxdtpp51MHEb5EgHrQ+kTQP1SLv8Eli9f7rVt29YbPny4t2rVKm/NmjXeRx995DVq1MgbO3ZsIkH4a9Omjde1a1evb9++3q+//uqtWLHC6927t9euXTv72XlesGCBd/bZZ3s9e/b0li1bZsMl/MaNG3vTp0+33pYuXeoNGzbMO+KII2z8zzzzjEcchPn44497zZs39+bPn++CDHXl2dGjR3tNmzb1OnfubNPm0tmjRw+vffv23sqVKxNhDRo0yGvWrJk3a9Ysb+3atTbvb7zxhnfaaad58+bNS/jr3r275TFt2jTvu+++s+HffPPNHnkYMWKE5ULacTC85557bN6JG54TJ070/vrXv9prIlDfh4ULF9ow/bx9P5fMx3KqS37oru6nK59ffvnFu+iii7x333038eiff/7p3Xbbbd6UKVPsvah1PpO6lIi8yD/QR5xzzjnejBkz0qYU5vQ7rn0l80yfcsEFF9j2RXuj3dH3dOjQIdEvEU/Dhg1t26SfeOSRR7xjjz3Wlhdlx/PpyjdZvNwjrG7dunmtW7e2fQb9yGeffWb7i0mTJiUec33iY489lsjL4sWLvSuuuML2dwmPvg9x5Z0gif+8885L9PP0y/Tl119/vffmm296H3/8cSJm+sVTTjnF++STT2y/SF9I/u6++27bv+Hx559/9t555x1vwIAB3pFHHmmfv/32273PP//c9vEHH3ywN27cOBsmTOgnr7zySu/3339PxEOc9JUwzMZVVZfI6zXXXGPfB9Qj/sjjYYcd5g0cODCbqPP+bKm+46KAokyqavNRwsu33+pQRo7pTz/95LVo0SLl2Mb507X6EdBKXYHk8gULFtiVKDYo16xZ065MHHHEEXYVjd+Cbs8997Szs8yIs4KFoQ9WIJidwjFz8/LLL5u9997bWklC15pwMQRy/vnn2xlhZnxr1aplZ86ZpWXWnH0NzAYRJv7q1Klj3nrrrWD0ab/zLLPKzEITf7t27eysNPex0oQKpd/a2dy5c03Dhg3tvgpmjVmV4TtpQ7/f71iZxJIoaSRPpBd/7MmAhXNYgvrxxx9N27Zt7X3CxMhJmzZtzHPPPVeSs8Mub1Vdy6kuVZXX4O9bbLGFbQv+1WDaRO3ate2qFP6j1vlyrku0C/6ydagpsveGtk47I0zaI5v32ZPGirpzznw9/QF+WHllfzBlx8phJm7s2LEGAwLdu3c322+/vV19osxx9C/O0Z8sX77cxun2CZEejohJtlrpnkt3jZL3WbNm2X6rSZMmtj+mD7v00kut2tQhhxxi+z3ioo98/vnn7Soc9+kXyVfXrl3N+++/n9BiQOWKtGO8iRXVN954w1xxxRW2j2QFkrpLv44jjMMOO8x+/vXXX+2Vf8RFeiiPbFxVdYm00e+TX9jzd+6551ojD9nEW4hn9Y4rBPVocVaXMmKsN2zYMNt3MI6TEwE/gezf7v7Q9Dk0AQYWqAWhbsPeBOd4oWPZKOh22203+5J2950w5L4zeOE8NZ5FmHMOf6glMegNqlYGw+Sle/DBB5vPP/88Y3PnwTCD6SRdbPBFzZTNveQda07sn/ILfi79+CEMHHvhkg1E6ORQ+2RwGfyd9CD0lMqeHpfvKNdyrUthGVDuU6ZMSZQxdR2hP9kxFMH6Gazz1b0uhWXOfjvaa3AfDkJb3bp1zVdffZUIisG/a8PchHk2G/tRO3r77betcOPfE4sq0siRIyv0n6SPY0v23Xdfmx4E/i+++KKSSngisSE+RMl7suBQo/L30fhBQN12223NXnvtVeER1M6ZMEOwC7pffvnF9nmOAYyZwPAL7ajgY6Vy+vTp9nEm9mbPnm2YJMylQ8USwRvh0z/5RhqzKftcpjls2ME+hDz5mROO3nFhaebGXzmXEX0BE1VYWQ/2I7mhqVBLiYCEugKVFi869jywL4wX7A033JCY3Q4KJmGSiMDGbCwzuUHHPV7mYQwfMPvLPohc7ntg4Dx58mS7z4Qzs9j3995779mOKpj2MN8xy0zeLrzwQjt45CXr/hjMsXLAPr5yddW5LlGmrBAjKHz99dd2xZqzBalXYZ2/zlf3uhSWGW2K/VH0La6tucEt+/H8E1Vhwwzrj76JPiooUBI/Qk1woMOEDpb9EI7Y34cRGOpKpi5K3tFcIH6EUJiwd++VV16xfT4rlc7RH+644462Hrt7XMkTA1S3X9T/GwIbAnQ6x/PkmRU8hGFWJ2GEcYxcOuJi4MlqY3V0escVf6mXYhkh0DGByZ7aYD9X/MSVwnwQkFCXD8op4th5553NU089ZWdheTmj/oj6IAOWcnV0pGz+J5+tWrWygiyqBMw6MVOdjRs4cKAd1BOH/+/111+3RgSyCbvYn62OdcmVCZMgqJwxcF6yZEksg8nqXJcc16quqIAj4PjbmvucD3P1fqEoVVrRAsDQAJNew4cPtyv6HGGCkZFsXNi8I/Sigk6fR//GJBOGU1ALz9eKFat/aCsw8cWER4MGDbLJeqRnw5RRpABLwDNtQO+44i6oUiwjJr4xMoUhMAl0xV2/Cpk6CXWFpP//M7EMyDmUFRVEVIiefvrpyKliLxz7NdweO38A3MNCUhihif0oqPPkyqISg6sXXnjB9OnTx6pPxdE5oWbHfppkexH9HMr9M7Py1aku+cuTPZmsZmB5NZXqpd+//7O/zqsu+cmk/sxqDy5Zf5P6qXh+QRjCKqffqq8/ZNLkVgpfffVVq9LIWU/0kXG4KHlH7XHGjBnmmWeesdZDSTNWfN3+P5ceVvRQp0ymIUG/xqQfq9GZONSz2buI5ct8qF6SRvoiygkBtro5veOKv8RLrYwQ6CZNmlRphY5JK7913OInrxTmmoCEulwTThH+t99+W2mjPisOZ5xxhn35J3u5pwjK3mZGFAMEqNkwC+V37NdgUMCA3+9Qj/E7Bg/jx4+35nN5KefCOcMFDMpSOTqpKOqSDB4wvPDhhx/agZE/XFSfECJRB8qVY29ky5Yt7bEMdLL5dtWpLmGA45tvvqmEmMkQBu2YW0+nellVnc9VXaIOsrcJlcVg+6yUmSK8QZscMWJEImWovNLn0LcEHe0QATtXDsGbvo6V2aBhJdr70KFDE+0ddUPUa1NNHtHPVDUoyjbv7Ju+7777bHpZLXNHPvj5sNKMwBU8lxNBjz4ZwybZOJ5HsGSyLteql6STeNgfjqqYE7C5T34YnGbqSqEdleM7Lkp5qYziHYewB9gZRPL3Y4xpsKMQdaxIWZZCGUWpc/L7PwIS6v7HIq+feLlzLhzW0dxLj5cBjQ0VHWfkgd9WrVqVGKS4RDIwZHDgBggIYag8MRv75JNP2vs8y/lQnIXEkn1wr97jjz9uBwwMbBBMevbsafd1sPckqgubTmaNeeGjEslgiedQCeIsOmZ1uUcHxuCMTos/8koa3WfSxuDcP7ONRTgG9T169LBWMPGDNUws9DHIZ7AedAz4mLH7+OOPqxzYBZ9N9p2y8xuISOYnF/fKrS45RqxMsMLALCX1gvLmc7IVZ8qXs3yoA+n28YSp85nUJZfmdFfq9JgxYwz79grlaEfEHxRug+lBSGaSh71nPIPRC4Qj59jHyRl0999/v+1jaMeuv0FFyO13o+36262/TeOfVbWgYObiSHc94YQTbLj0G9QLHFfO4KT/dH0dq7YMfOhnnR9U41D5pu8kf0EhI668Ex/7pVG/pH9lLx8q9tRfjFexquwcQnKLFi3sOwFDP3Cj/6I/w3gVApLfBbn6f0v2mX15GF3BqFBcrqq6hOVlJpyGDBli6wb1H0vETOjRDlGHzcQVqh1RX8O8i4vpHReWL/0rk2WsLMexuqoyCj8OSVdG9I3//ve/bb9Bv81ZofzRnzB5xXgj3QR5urALVUbp0qTfYiBQ/U5xKI4cz5w5057ndNNNN3n16tVjac2eHcc5PpyThXPnqPEbf+5sH85RcvcaNGhQ4cwpzg7q2LGjV7duXa927dr2nKO5c+dWyLQ7/2vUqFH2DCH88terVy97vl0FzyG+RE3n119/7bVq1cqrVauWjffOO++0eeUcvu23396eZUReXR7JL2fW8Z2z8GDHle/cd4589evXz6tfv779jTP3/GdWOX9+fi6OYFjOb9jrkiVLvEsuuaTCuVNhn83WXznVpSAL6jNndVFXKE/ONkzlJk+e7I0cOTLpz1HrfNi6lDSyFDc5Myx4ZmMKr7Hf9renMHWeM86GDh1q2xJ9Q//+/RP9kj9xnAXob8ucnwk7nL9f4Pwr2iL9FfGTHpemYB/mDz/d50WLFnl33XVXov/k7EvOaCPtztGXcqab6xPwM2bMGG/27Nn2/E7OegqeyxlH3ol/9erVtk8dP368S469cqYf91q2bOlxZp5z3KdPbtKkiWXEWaL+9wH+HDN/GYbpu0gLZ/XRT2XroqSBd8+ll16aaL/Ugfvvv9++d9zZqVHTU4h25K/Lrv6Sbv+7xF+PC/2OC8vU9YvB+uTPS9iw/P5URlWPQ/y8Un3mnE/XZwbLiO+M2TJ1hSijTNOq58ITqIHXGGRDBVFCBJihbt++vTUggNqiXPYEUJHAuEanTp0y3vuSfSryH0Ix1aWXXnrJrkQkswhY6HQyy8+qEu1NbS7/9bQQMbJyiEEo+gTOkPM7Zto7duxo7rrrrrwYcWLVDzXQfBiw8ecz7s9qR3ETjT88lVH8TOMOUWUUN9HiCU/ql8VTFnlLCTrYqD1looudt0SWUESodqFKhAXPTI0ZlFB2KyS10HWJPV2YiEdlCPUUVOeSuUKmE5Ut1M3YN8W5XXLVgwAqqlia7Nu3r90rx/wpf6hPYQwLVeFc7m9DvYqJJgwBTZgwIXEoeanSVzsq/pJTGamMip9Amacw/KKefJYDgeByvl990Z+/oL9kS//uHiooctWPQLCOFKIuPfLII16dOnW8tm3beqjjJXNh05nsWd3LDwGnXu36lFRX1Didemd+UpZdLCtWrPBefPHFhEol+Tr++OOtmniu84HqI6qcqJ6+/vrrFdRSXa6CbSMVd+6rn3fUivdaru2oeIlHT5nKKDozPRGegNQvy1xoV/ZEQAREQAREQAREQAREQATKm4DUL8u7fJU7ERABERABERABERABERCBMicgoa7MC1jZEwEREAEREAEREAEREAERKG8CEurKu3yVOxEQAREQAREQAREQAREQgTInIKGuzAtY2RMBERABERABERABERABEShvAhLqYixfzh5q2bKlOeecc8xPP/0UY8jlFdQLL7xgdtllFzN48GBr4jufuVMZ5ZN2ZnEVuowKWT8zI6an8klA9TOftBWXCBSWQKHbe2Fzr9hLjYCEuhyUGIPCr776Kgchl0+QnKE0ZswYe7ZYIXKlMioE9WhxFrKMCl0/o5GS70IQUP0sBHXFKQKFIVDI9l6YHCvWUiRQsxQTXaxp3mabbcyjjz5qD6DecMMNizWZBU9XixYtzKabbmpGjRpl1l133bymR2WUV9wZRVboMipk/cwIWBE/9MUXX5hvvvnGfPnll6ZNmza2byzi5IZKmupnKEzyVOQEvv32WzN58mQzbdo00759+9ja5sqVK82kSZPMb7/9Zj766COzySabmBtuuKHIaaROXqHbe+qU6RcRqExAK3WVmWR1Z968eWaHHXYw++23X1bhlOLDrG706dPH7LPPPqZGjRrmpJNOMqNHj66kYrl69WozdepUc9ZZZ5n1118/71mNWkZ//vmn6dixoznmmGO0Apun0opaRnEmK5P6+dlnn5kDDjjA9OrVy6xZsybO5JR0WAh0jz/+uJkxY0ZJ5yOYeNXPIBF9LzUCP/74o3n55ZfN3LlzY036qlWrDP3hp59+av8YFxS7Q8WSCfk//vgjaVIL2d6TJkg3RSAFAQl1KcBkcnvmzJlmyJAh5sYbb4xt1iuTdBTiGTrDzp07271yU6ZMsQPbO+64wwpDrMg5R4dP53nooYeao48+2t3O2zWTMlpvvfXMKaecYq666iqz4447pk0rM5Pjxo1L66cYfizmdGZSRnExzbR+skeU2e6TTz457erzihUrzBNPPJFy8BBXPoolnNNOO82cffbZkZOj+pkcmepnci5x3C3HtpmuHR155JHm8ssvjwNdhTDQwuFdecstt2TU9isElsMvlDdjlhNPPNFORLO6mMwV8n2ULD26JwLpCEj9Mh2diL/Vq1fPCjERHysL78z2MTN30UUXJVbfeGlcccUVZuDAgeaoo44ytWrVMghI11xzTcHynEkZsep47LHH2r+qEv7111+bXXfdtSpvBf+9mNOZSRnFBTTT+lm7du1QA6Rly5YZZsjl0hNQ/UzOR/UzOZc47pZj2yzmdhRHmWUTxgYbbGA6dOhg2rVrZ/r162dYjUvmCvk+SpYe3ROBdAS0UpeOTgn95nmeQYWgUKoO22+/vTnssMMqGT5BFZNVvLVr15YQzcpJ/fXXX82cOXMq//D/d+A/ceJE07dv35R+ovyAyucPP/wQ5ZFQfuNOZ6hIq4EnVDa/++47u48kVXZpmw899JD5/vvvU3kJfb/Q7Z32/NZbb9k67080+2lQ6Vq0aJH/dujPqp+hUUXyWN3qZyQ4xtj3Zrm0TfKudlR1DWCydvPNNzfsmdtjjz2qfkA+RKAECEioK0AhoRJx++23m06dOtmjD+699167uoMAhGoiL2C/Q1i7++67E3vV8Pfggw8mVLgQmq6++mqr+vXmm2/avWyHH364XTkjPFaOWEEjHL/DeMHFF19sNttsM3PggQea/v37J8L0+wvzmTB69uxpV+Scf14sgbtw5AAAIABJREFUqGIecsgh1jCKu18qVwamqF2iWrf77rubZ555JmnSP/jgA7POOusYmI8fP97uveOFwd+zzz5b6RkGvgMGDLDM4UYZsMfQOcJjFpHyYI8WezRRZWVD+5lnnmnL88UXX3TeQ1/DphN1k4MOOsimnyvfqWOo1Lh8UX/nz59vevfube8TNqrH1KOtttrKzoDye9BVlfeg/2L/TvlShszmnn766Ulnex071JK6detm2/jGG29sWVK/kglA6dpmpu09LMvFixfbvol+g7w1b97c3HbbbbYeEAb1gTaNqimTD85RBzAQRb1N5hBqH3jgAVt/qSdPPvlkhb5O9TMZtezulWP9pA8ZNGiQQROE/ui4446rsHebFTfqIP0VqvC8b2mb1GWOG6JtOZdJ23TPJrtm0jZ5T7L3nD3o5Id8kT/y6VyUMUPYduTC5kofxHiENs9fsnGI339cn6sa2/jjYdLssssus+VIGkkvquz0JVzlREAE/juj48nll8CcOXO8++67z2vRooV3yy23eBMnTvTWrFnjTZ061Tv++OO9jz/+OJGgBQsWeGeffbb32GOPecuXL7f3Fy9e7F1xxRXe448/nvA3Y8YMr2HDht4999zjrVixwnvkkUe8Y4891nv33Xe9X375xbvgggu8sWPHJvxPnz7d3nNx//rrr17Pnj29Dh062OcTHjP8sGzZMu+ll17yrr32Wo88lLp7+umnve7du6fNBuXTpk2bCpyDD6xatcqWEaxhTrlTBn/961/t1fknrkaNGnnTpk3zvvvuO69p06bezTff7C1dutQbMWKEjcfVB/dM2GuYdFJnLrroIlt/XLh//vmnd9ttt3lTpkyxt0jLsGHDvCOOOMJr27at98wzz9g6Sv2jbjZv3tybP3++e9wLm/fEAyX0gTp+zjnneLTDdI42SB1JV3Zh2mbU9p4uTf7fSBdlOXz4cFte1M+PPvrI1kV//0F+TzrppEp1/dNPP/XOPPNMb+HChYlgBw4c6LVs2dLr1KmT5UOY3377ra0fgwYNSvhzH1Q/HYn4ruVSP+lbunXr5rVu3dqbN2+et3btWu+zzz6z9XPSpEkWGH5Gjx5t+8zOnTt7vXv3tn0t93v06OG1b9/eW7lyZSW4YdpmpYeS3IjaNmkDp5xyivfJJ5/Y/JAv8nf33XfbNkgUUcYMLklh2hF5ZhzSsWPHxDhk5syZtm2PHz/eBZXxlbaf6r1JnQwztiFy/J533nmJfonxRdeuXb3rr7/ee/PNNyuMmTJJLOmsql/OJFw9IwL5JqCVugKI9nXr1rWzccuXLzfXX3+9NRrCSs/+++9vGjduXMFS3NKlSw3+sLy40UYb2dRuueWW1l9QB5z7Z5xxhl3lYVaSZxo1amS22GILu5LgsupmzNElx2AJceO/devWZuHChXZFyPnN5MoqDqsSnOuC8QjUG0rdxXX0wrvvvmv3VLVt29Yyhz1lgLn35557rsLKBzO3WFGl3DELfcEFF9h9iawaUl65dNSZpk2bVpjVZgacvWOsSuHYI8lqDat5rFKSPtLKKuP5559v6tSpY1X0XDqj5N09UypXypG/bF2Uthm2vUdJ04IFC2z5YTygZs2aNk9HHHGEXZHlN+fIa7I2wQpkMou2mE7nqAjqDs+y+n3hhReaESNGpFVZdfEFr6qfQSLpv5dL/Rw7dqz55JNPTPfu3Q0q/6xs0SfhnBVH+h9Wu1jN2Xvvve2eKfpL7rPqjFo75vZz6cK2TdLy/PPPm3vuuceufpMf8tW1a1fz/vvvWyuSpDPKmCFqvhhfXHvttYmxwF577WXHDf4VzahhhvEfZWwza9Ys+w5s0qSJ7Zd4H1566aUGrQK0Bho2bBgmSvkRgbInkP0opOwR5S6DO+20k9XpdjHQoQcHSuh6v/baa2bfffe13hhYc/ZTsg6XFzdhOMcAOxgev7Gnh5daUI+cFx8vj2wPTseiFKojCHQIL4MHD650rIFLY3W6omaDOhBCHAMMv9ttt90Mg2YG9c5Rdq48GSgHn3H+cnUlnajPujRR5xAokw3aSb9LK+mh7h188MHm888/t5ZQo+Y9V3kq9nCjtM2w7T1KnhmMohKFOphfDZyB06mnnholqAp+mVzac889K9xjwoI6/9NPP1W4H/aL6mdYUvH5K2T95KiQt99+205obrfddolM8R4dOXJk0voZ7Jfoo2g3uXZh2yYC6rbbbmsQpPwOlXsEUwQ7vwszZvD7D/M5F2GGiTfK2CZZeBgNYuJJTgRE4H8Ect+7/S8ufcqQAINqrDPRybMywoZurFpl6liNQ9Daeuut7UCcF5172bHnxz+YyzQOBv6s3jDjyP6GdEZGMo2j1J7DhDKDIlYoHHN3RWinXIrJoAwz3Qhn1DWEMvb9cRZbWMdqLTOp7LsqtbyHzWPc/vLRNtOlmYkdVtrZs4kQxqHB7OXEZTOp4J+gcPFT96lXmZ7rp/rpSObvWsj6ST9CfxKcjKQeoTVQigN8VrA5Jod+1u/IEwKp28/s/62cPocd27Diil+EesYn7F185ZVXbB/Fqr2cCIjAfwlIqCvymsAsNkZOMDoxfPhwu9Lz8MMPWwMe2SQd1Tpe0Ayqgn+o0cXleAFjPGHChAlxBVny4XDEQ5A5319//XVrZKRYMsggHsGcF+mSJUusGjCqQdm4Usl7NnnM9tl8tc1U6dx5553NU089ZVcJWLlHlZYVdwbUxeRUPwtTGoWunxrEF6bc4441ytiGCWhUZzEgw8omk6AYTmHbQjJtpLjTqvBEoFQISKgr8pJ69dVXrWoGKo3sUYrDMauJQ5UzLkcHjV6+34qjP+w4Vv/84ZXiZ1Yv2f/h35tU7PlgrwKzxVhfS6V6mSoP7HFBTQqhvhTznipfubyfi7aZSXpZKUC4u+666+yxBahoPf3004mgUC/LdmUEFW3ioX5k6lQ/MyWX2XOFrJ8M3tmzmUrrg/dZqb1nWIH65ZdfKuyldiXDe4JJleAqnvu91K9RxjbTp0+3tgawQI2lTuoAFqHdfspSZ6H0i0BcBCTUxUUyR+FgDAU1tlQDKNT1/KaPwyQDtSVmO9HnD7oPP/wwYbo8+Fu67+zDw6Q9Lyi/Y2DPiiBGYKI69va0bNnSmqHOdN9N1Djj9o/q2jfffGODZVDC3iIYBzmhWoJhmUxV0bJNtz+d/rAYzDOZgGn0dKqXbLb3OwYkHO/Aoe0M3HOVd5hhdKNU923SdjEW4lwu2qYLO8z122+/rXQsAytiGGBiMOWOMEAQo24E6yvPs6obdNSP4ICbPgPDKf79UcHn3HfVT0civ9diqp9MDGH8C80BjGz4Hf3n0KFDK9VHv5+on4N5j/p8GP9oQsyePbvSmaS8H+g/MQwSp0vVjuKMI2xYUcc27PO97777bPmzjQEVzGJzpf4+KjaeSk90AhLqojOL5QkGRwyIULtzjs/c44Xl7rM6MmrUKIP1JxydPSoIw4YNs50a+50mTZpk/fvD47P7zmCKWUz3ImTfDOfa3X///YZz7fidPz5/+umnlfYsuPSlu7L/hhcu6SMshE1WeFhhxOpdOoEgXbj8RkeZrfGWquKo6ndYVjUTzIwqg9T33nvPCtqw4DPqIs5h3RQhqUePHtYKJvd//PFHe34XjBB+XD1w5QdL9xn/DJAJ2w2wXdhhr2HS6cIiPQhmpDmd6uXjjz9uByGkFWGcMwvZK8I+UOfC5N35jXJlQDdmzJhKB99HCSNbv+SbfYNB4TYYLoIQAi/tlmew5sekjXNh26arI66foH64OhJs7y7sMFcGSx06dLD9DeHg6Ddog6g8MbDGOaEOYZq6SF5YzWX2nfLnfEX/OYXkl0kfBmLOb9++fe2ETXCvnuqnRRzrv3KpnyeccIKt59Qv6h2OK+dlUj9dXaLurlq1yvr1g6S9UAeTCQRVtU1/OOk+R2mbTOLwfqTNYYiKZ3kf8H7A0BQGipwLO2Zw/sO0o6hhurDDXumTUr03w45tiIvxBeqX9C3YFEAlnPcqZxTS72TjEN6ZeGU1EJXObF0xvI+yzYOeL2EC+T5DQfF5HmeicEQgf5w/xplO7kwZd9+d7cLZXgMGDPDq169v/Tdr1swbM2aMN3v2bK9x48b2jJkvvvjChsOznLXCeT0NGjSw/onLxcc9/zlanDfWqlUrr1atWl7dunXteT6kI1PHWTKcd0NYpKVJkyb2rDrykKlbsmSJd8kll2R9Dk0m8VMulI8rE3cNcvSHDQPOGIIp57TNmjXL/7P9DON+/folyhR/7owlPLjyIj7OEaIu8Jm0cIaQS5OrI5UiCHEjTDpdMJMnT/ZGjhzpvla4uno7atQoW38oe/569erlcZZQ0FWV96D/MN85ty/V2VNhns/Gj7+sXP3gmqpsOFdr6NChtuzh1L9//8RZVP50pGub/noZpb37w0/1mfrFWZU33XSTV69ePVvvOIeQs7SC7dhfh+ifnnjiCXvW5j//+U/v7bfftmcqEs/LL79s+50PPvjA9gm0Dc7G8tf5YHr8YadqR+4Z1U9HovK13OonOVy0aJF31113Jeon78Rx48bZM9743d8+aIswwNGXujaarA8P2zZtYCn++eMO2zY5t5H+k/cl6UvW3vzlWNWYwZ+0dO0o0zD94Sf7zBjDjT8cb3elDJwLM7bhnNPVq1fb90nw3Dy4cY8zMDm7N6pz7y6XNndNVjeihF3I91GUdMpveRKoQbZKWCZV0sucAMc3YFyjU6dOZbu3oNiL8KWXXrLHMLC/I+iY8eboCoz5oFqab8eMPLP2xF2I+POdX8VXmYDqZ2UmuiMC5UKAlVgMNTEG2GeffSpki5W7jh07mrvuuqsojIzpfVShePSlAASkflkA6IoyHAFUTrH4eeONN0qgC4csNl/st8RkNOooqBWimpTMob6Dek2mqqDJwgx7D/Uq1D7Zl3L00UeHfUz+yoCA6mcZFKKyIAIhCKCS3qBBA4O6Noe1sw7BH3t8Md7E1oDNN988REi59aL3UW75KvSQBMpzAVK5ypZAOhUKp6bgrn6Vimzj1fPhCeSyjB555BGvTp06Xtu2ba26U7JUBeNPpXKY7Fndyw8Bp7rr2mqqK6piqCOVilP9LJWSSp/OUqmfpZLO9LQz+7UY8r5ixQrvxRdfTKio0o8df/zxdhuD67eKIZ2ZEdZTIhAfAalfhhR+5U0EREAEREAEREAEREAEREAEipGA1C+LsVSUJhEQAREQAREQAREQAREQAREISUBCXUhQ8iYCIiACIiACIiACIiACIiACxUhAQl0xlorSJAIiIAIiIAIiIAIiIAIiIAIhCUioCwlK3kRABERABERABERABERABESgGAlIqIuxVDgzpWXLluacc84xP/30U4whKygREIFkBF544QWzyy67mMGDB1sz18n85Oqe2nuuyCpcEUhOoJDtPXmKdFcEREAEioeAhLoclAUvnq+++ioHIStIERCBIIHff//djBkzxp6nF/wtH9/V3vNBWXGIwH8JFLq9qxxEQAREoFgJ1CzWhJViurbZZhvz6KOP2oOyN9xww1LMgtJcBARWrlxpJk2aZAWVxo0bmyOPPDK2VH377bdm9uzZZt68eeb99983N9xwg9lrr71iCz/fAbVo0cJsuummZtSoUWbdddfNa/Rq73nFXbaRqb2HL9pCtvfwqZRPERABESgMAa3UxcydwfIOO+xg9ttvv1Ah//nnn6Zjx47mmGOO0epeKGLl72nVqlVm/PjxVqVwzZo1sWb4xx9/tGFPmzbNTJ061axduzbW8OMKjIHuSy+9ZI477jhTo0YNs88++5i7777bLF68uEIUq1evtvk466yzzPrrr1/ht3x8UXvPB+XyjkPt3RhW3/r06WPbOe39pJNOMqNHj66kUl3o9l7eNVG5EwERKHUCWqmLsQRnzpxphgwZYm688Ua7Whcm6PXWW8+ccsopZt999zU77rhj2kc++ugj+5I76qij0vorxx/LLe8rVqwwzz77rDn//PMr1RVWnq688krzxRdfxF6UrPrxt2jRIrtiF3sEMQSIQNetWzez2267mZEjR5qNN97YTni0b9/e/PDDD+bee++1zBgMP/bYY+bQQw81Rx99dAwxRwtC7T0aryi+1d6j0ErttxTa+x9//GE6d+5sjj32WDNlyhRTs2ZNM2HCBEN7px/4y1/+YjNY6PaemrJ+EQEREIHiIKCVuhjLoV69enbVrXbt2qFDZVaSl9mFF15YaXAfDOTrr78u2pWVYFrj/l5ueV+2bJlh1UyuMgGEJf7OPPNMs8kmm9iVOlREu3TpYlVGv/zyS/sQEyLXXHONOfHEE62fyiHl9o7ae+74qr3njm2xhTx37lzz6aefml133dWutq+zzjp24umKK64wAwcONL/99ptNcqHbe7FxU3pEQAREIEhAQl2QSAG+//rrr2bOnDkpY/Y8z0ycONH07ds3pZ8oP6DyyYpHKbhyzDuqRg899JD5/vvvS6EI8p5GBnGffPKJmTFjRoW4N998c/vdDfIq/FhCX9TeUxeW2ntqNuX6y/bbb28OO+ywSoaOULlmFa9YVcTLtTyULxEQgdIlIKGuQGWH+htql5hj33333c0zzzyTNCUffPCBYeby8MMPt3uh2HvH6h5/qO8FHaprAwYMMAceeKDZbLPNzMUXX2z3HDl/hLfBBhuY/v37m169etn9f3fccYdhjxUrI8yWvvjii8575CvCKaqDW221lf1jv6B/HxRqVbfffrvp1KmTPfYBVTri5AWOkRn2TDgXNe/uuVTXTPJO2kkraSRP5M0vgLPiBsurrrrKjBs3zpC/008/3bLnaAu3qkSaGKDgD/VK1IrIL6qFlCV1gTqRzLG3hD0m+KOM/GEm8x/HPQZS77zzjmnevLnNC3WJ/Hz22WeVgqfMOFKAOkcaSSsWIS+77DLTtGnTyCuS+++/v1XHYrDndwz4MU6y9dZb+2+XxGe1d7V3tffkTZW+pWfPnsa/rYC2jirmIYccYvvL5E/qrgiIgAiIQAUCnlzBCTz99NNe9+7d06Zj+fLlXps2bbyxY8em9Ldq1Srvnnvu8Xr27On9+uuv3po1a7yJEyd6f/3rX+3VPUhcjRo18qZNm+Z99913XtOmTb2bb77ZW7p0qTdixAgbD/FFdZMnT/aOPvpob/jw4R5pIYxu3brZsFeuXGmDmzNnjnffffd5LVq08G655RabLtI5depU7/jjj/c+/vjjStGGyXulh1LciJL3BQsWeGeffbbluWzZMpsn8ta4cWNv+vTpNoYVK1Z4o0ePtgw7d+7s9e7d27Lnfo8ePbz27dt7Lu/+JFGOlGcqzi7PXbt29fr27ZsIk/DbtWvnEX42buHChV7Lli29GTNmJA1m0KBBXrNmzbxZs2Z5a9eutXXpjTfe8E477TRv3rx5FZ7B75VXXmnv43fChAneCSec4L366qveCy+84MEuDjdw4EDvxhtvzDrvcaQlmzDU3tXeg/VH7f1/ROgvXnrpJe/aa6/16IPlREAEREAEwhHQSl0FEbcwX+Iyxf7uu+/aVZG2bdva1RVW+DAi0aZNG/Pcc88Z1C6dYzUFC50bbbSR3bd0wQUXmFq1atlVQ2ZOozpUCh944AFz9dVXm2bNmtnN7oTN/sJvvvnGrlIRZt26de1+ieXLl5vrr7/epo90sjqD+f6gyl3UdITxHybvzBS//PLLZu+997bpZG8XG/jJG8ZNnnjiCcOqKKueGCNgJQ+/7dq1s+y5zyoXaq7ZqAvuueeedt8YZUKYrHzNnz/fsEKYS8c+l4YNG9r6wOobZcR36sjSpUsTUbP6yBlx1B9W1vCLKhWrlUuWLDFnn322rV+JBzL8AMdXXnnFXH755ZZDhsEUxWNq72rvqSpidW/vaHGgycBKP4ZSWJmXEwEREAERCEdAQl04TkXvCyEE9T+EOAb/focVwQULFliz0e4+A0sG4DhMwQefcf7CXjFsgOEPjL64cHkWdcUnn3zSuP1QLryddtqpwj2eiWuw6+JIdQ2TdwQXzj479dRTrTDnwiKdmNlHBRLhyu/g7M87nxGGsnG5CDNMem6++WZz22232TJBvfKnn36y+zrDCqiolcblmDDAyiUqq1iJlTPWCq7ae7iaoPZeNadiae9YwWSyDIGOyUnUunm3yYmACIiACFRNILsRZ9Xhy0eeCGAiH8MbWNFEmPD/MRBeuHBhTjecM9hnFWeLLbaokGMERmZeS80hsGHQItn+Le4x8ChnQycMpCZPnmz3xR1wwAFWOH/vvfcMK6x+x2osK6zs70So57lZs2bZ1TsmGLJ1CHTs/WR1FGuTcv8loPYeb01Qey+O9k6p8s5gD3nXrl3tfmX/HuZ4S12hiYAIiEB5EZBQV17laU1AM7AO/r3++uvW0Ecus4tAh9lpudImQN0ZNGiQnSlv1aqVNaIzbNgwe/7itttuWylzJ5xwglXjPffcc+3K5N///nfTunVrq45ayXOEGwh0GNLBQIsEuuTgMPkebOt8V3tPzkt3KxMolvYeTNkee+xhNtxwQ3tmXfA3fRcBERABEahMQIePV2ZSkneY3WT/GmqWhXC8fFmpYSAeVL1DfY+VhVJasatTp47dC5Zs7xr3yG8yAacQ7OOOk1UL9rT06dPHHHzwwWmDX7Nmjd1fiKXL3r17p/Ub5UfqERbxWrZsWUGgY4WUvXrlyj4sI7X3sKTC+VN7L1x7R7WbvXTs/a5fv36lAvNbRK70o26IgAiIgAgkCGilLoGi9D5wDAFGSHDsG2nUqJH58MMPzS+//FIhMwyQGaQzAM+V41gGVPEmTZpUKYrx48fHbobfn/dKEcZwg1VHjo/A+Awz2X7HGWoYfNl55539t7P6jLAyYsSIrMKI62H2E6JmGRTO/eGTXnd+FAI7Ah3GeDjygPrnfvM/E/Yzgzj20LECGNxD99VXX1n1zrBhOX8///yzFRBZ9WMQWYrOX+fV3uMtQbX3wrV32vSQIUMqvbcw1sS2AYxoRXXl0N6j5ln+RUAEREBCXRHUAYQtVn/SzUgiMKGCxr4mBtQMnPnsX7FgbxMzzj169LCrZmSN1TOsUrIvioEgAgrx8cdnBt/uM/4ZzBO231JmGERbbrmlQVWve/fu1mALeeEPYw6cs4alTecI2x8n9126ECiCQlSYvLuw011dHC7udHlnTyIWHTnbDkMvWHkkP2+++aZdmeK8OWdchvurVq2yefLHT3w8x1/QYSiGVVUMzJCOsWPHVljJzCTMYBzpvhMnwlhwjxzPUJasRKLCR10jLVOnTrV727777jt774033rCrstSp8847z+6DgdUtt9xiz5vCah2GVphQiOqoL6xEUWc53839Mfjr169fBQM7UcNmcoNwCumof2rv/+2H1N7/WxOrc3vH4icTaLx34EDfNHPmTHtWZYsWLWw/kGl7LYb2nmna9ZwIiIAIRCYQ7uQD+YqbAOeEcT4c8oz/r0GDBinPDuPMntatW3u1atXymjdvbs8QC6aL84769evn1a9f34aLv0mTJiW8cdaXi4+z0ji3je+kZebMmYk0VXVuXiJA3wfOKBszZoz3t7/9zaaxbt269tw8zr9zzh8/ccLBndHk0pUs7jB5d3GkuvrjDpt34u3YsaNHXmrXrm35z507NxFFsByJA0f4Lj/JyhRWQ4cOteVE2P3797fn4PFspmEmEpXmgytvlzZ3DZ6Z9/XXX3utWrVKlOOdd95p08WZedtvv73H2XTkgXPsunTpUunsOM6auvXWW73BgwenSU3yn1KlkbRyDiJnHWbilixZ4l1yySVJz0LMJLwozwTL1HFPVjdcuGHqvNq7o1X5qvbuJfp3V9/ctZjaOyXn72dJY5MmTexZdZx3mqkrZHvPNM16TgREQASyIVCDhyNLgnpABERABIwxzz//vPniiy+spTpWN/3u3//+t10dZsWzGBzpxLBIp06drKpwMaRJaRCBUiKg9l5KpaW0ioAIVDcCUr+sbiWu/IpAjAQOOeQQq6L66quvJlR2Ua+dMGGCPSw8meGDGKMPHRTHLAwfPtxa8ESdV04ERCA6AbX36Mz0hAiIgAjki4CEunyRLrF42NNw0EEHVTjvzn/2nf8ze6kK5UolnbngUwx532uvvcy//vUv884779gjDKgXe++9tzV00qVLF3PggQfa/TGFrkukif1+WIiVq0ygGOpS5VRVvlMq6ayc8uzvFEPe1d6zL0eFIAIiIAK5IiD1y1yRVbgiIAIiIAIiIAIiIAIiIAIikAcCWqnLA2RFIQIiIAIiIAIiIAIiIAIiIAK5IiChLldkFa4IiIAIiIAIiIAIiIAIiIAI5IGAhLo8QFYUIiACIiACIiACIiACIiACIpArAhLqckVW4YqACIiACIiACIiACIiACIhAHghIqIsR8s8//2xatmxpzjnnHPPTTz/FGLKCEgEREAERiEpAfXJUYvIvAiIgAiJQqgQk1OWg5F544QXz1Vdf5SBkBSkCIiACIhCVgPrkqMTkXwREQAREoNQI1Cy1BBdzerfZZhvz6KOPGg433nDDDSMldeXKlWbSpEn2TK8tt9zSnHHGGZGeL6Tn3377zUyePNlMnDjRHHfccaZhw4Zpk+PyynMfffSR2WSTTcwNN9yQ9pk4fnTxjhs3LlQ644hTYYhAKRFwbSTfbTNXjLLpk3OVJoUrAiIgAiIgArkgoJW6mKnOmzfP7LDDDma//faLFPKqVavM+PHjzQMPPGAYUJWS+/33383o0aNNnz59zIoVK6pMOnn97LPPzKeffmr/eD4fzjF++umnQ6UzH2lSHOkJUE8OOOAA06tXL7NmzZr0nvVr1gQK1TazSTgqlkym/fHHH0mDybRPThqYboqACIiACIhAkRKQUBdjwcycOdMMGTLE3HjjjXa1LkrQm266qbnuuuvMWWedFeWxpH4RrJ7n4AdbAAAgAElEQVR44omUg5ykD2Vxc7vttjPXXHON2WeffUKFQl6vuuoqc8stt5izzz471DNxeCLeK6+80hx55JFxBKcw8kBgl112Me3btzcnn3yyWXfddVPGmO86nzIhBfghzrwXqm1GxUaeO3fubE488UTb76DlkMxl0ycnC0/3REAEREAERKBYCUj9MsaSqVevnunYsWOMIWYW1LJly8yPP/6Y2cN6SgSKiEDt2rXN5ZdfXmWKqnOdr45532CDDUyHDh1Mu3btTL9+/QyrcclcsfTJydKmeyIgAiIgAiIQJwGt1MVJswjCQpXxoYceMt9//30RpEZJCBL45ZdfzOOPP15pFRXDOqNGjUp4//PPP80PP/yQ+F5dP6xevdp89913aVWS46zznucZ1PnypRKcbbnGmfds05LP52vUqGE233xzw565PfbYI59RKy4REAEREAERKEoCEuoKWCyLFy82nTp1Mrvuuqs58MADzeDBgw2D+aBjkHn33XdbNSMGM6g5PvjggxUEA/aToNKI+lS3bt3sHpONN97Y4P+UU04xixYtCgZrvvzyS3PxxRebzTbbzMbfv3//CmFWeiDkDfbKnXnmmTZcwv/mm29CPlnZG4Ns9uuddNJJNi+oTg4aNMhg0CHouMdv+CHfGG3hWcJI5eACH/zz9+yzz6bymvQ+qyRwgz0GWDD8cvrpp9u8c7QFjJ27/fbbDStPH3/8sbtlefPsXnvtZRYsWGDvf/DBB4aVCMJlLxl7NO+44w4zbdo0y5X68uKLLybCiPIhTF2CgeNx0EEHWeM9xOG/TxqdQ/Ci7lKHeY6ywtrgZZddZpo2bZrRqjFxUS9ZaYFnspWYuOs84V199dVW1fPNN9+0+Tj88MPtvk/2bMH9oosuskKfy3uUa1X1M0pdyiTvUdIaxm+YuuTCQTCnPlCmcKTfQ0WcOsNVTgREQAREQAREIDsCEuqy45fx08ywd+nSxc40f/HFF9ZICoG98cYbFcJk4PT3v//dbLXVVtY6JgIKwsPUqVPNc889l/CLxU2EAH4fO3asadOmjVm+fLn9/vrrr9vnE56NMcSJ8Ne2bVuzZMkS+wyDStKUTGDyP5vuM0IUaXvmmWcMQmuTJk3s3jnykYl7/vnnTc+ePc29995r1q5da4YOHWreeecd07t3b4Mw4Rxpvueee+xv+MFv3759bX6wzJnKwZWwbrrpJpveCy64IJXXpPfXW289K3jMmTPHjBw50pYjAgn5Pfjgg61qmBPU2QOEkRa/o9xYWb355psTtxs1amS6d+9u84JQNGHCBFs+MOV51M3eeuutyAJ42LoEg08++cQ0btzYCmsIVrjzzz/f3HnnnWbMmDGGNDqHgEmZUHfhftddd9k0sj+0devWdiDv/Ia9koalS5daIdnFH3w27jpPeOzfq1mzphXGX3vtNXPppZea66+/3uy7777WuA/ta9asWcGkVPk9TP2MUpei5r3KBEb0ELYuESx+2T/bvHlz28Y+//xzy5hJivvvv98wcSAnAiIgAiIgAiKQHQEJddnxy/hpBsBYuWTQyACNv3PPPdeceuqpFcJkYItwdswxxySMr3DkAQPuZKsXFR5O8QWBEiub7Ec59NBDzTrrrGMH3gzAFy5caFeEUjxa5W0MGDAg55gCBsessmC1ECuGUR3qhwh1CGuHHHKIXQXafvvtTdeuXc37779fIUwEWQQRhCH8sGLEqhhu7ty5KaPmkPjhw4dbowtwjepYUWNlkNWHvffe2zJlNYL7DGLJg9+aaTJjH3BihTXoWPHCiip1A55wrVWrltl9990zEpSi1CUEKeLxq4BSD1F38xuaYcUIIY+0Oe6HHXaYLXcmCzCEQ9ozddRN/rJ1Ueq8O1KEMqQsaXsIsVtssYUV4DNJS5j6GbUuZZKOuJ6JUpcQgqkDTPBQ1/lMv8ekD+26qiNQ4kqzwhEBERABERCBciaQ/WipnOnkKG+s3DDIO/rooysMzhFEgoN+9ouwYsBKAY7VNFbZ/Gp9UZPJfjsEjeBeFAawdevWzergdFaWGLg5x0AYFSu/yqH7raorQtq2225rVRP9flFHRLBAsMMhNL799ttW0MUSp3M77bSTXT0LCsrud1YQWCVr1aqVFZbc/Uyvu+22mxUm3fOUZzYCCXWBMHDrr7++FRRd2Jlco9QlBt7UT7i6owS+/vprqwqK8FGVQ/W3mFyUOk+ZOe7kAaE62C6j5C2T+hl3XYqS3jB+o9SlZOGxKunvJ5L50T0REAEREAEREIHwBCTUhWcVm08Geax6sLIRxrHKgModggym3RFEGGBn6liNYw/U1ltvbQevDGCdAIJKpl+tMWocwcEv4XIvkzBRm9xxxx0TK5QuLYTJoBdz5awUISQz6x8UUvHHylaywSMrDahdIlxnw9KlqVSuUeoSK1RTpkyxK52oHXK4PGfG+R0CD6vGqJxicdWpJ7J6xypwsbhc1vmq8phJ/awqzGL4PWxdYgUbv0wQ0A/QZl955RWz55572tXPYsiL0iACIiACIiACpU5AQl0BS5BVrKoc6oEYZ5g/f75VE2Q/3cMPP2yNe1T1bLrfWVFjoMsgPPiHKl0pujA8Xb6efPJJqyrJPjf23rFqV+4ual3ijDhWO9knSf3DqEyyiYgTTjjBCt6oD7PKxR5QVHkZzBeTK3Sdj1I/i4lbsrREqUtMHqGKjBEjVt7ROsBwCvt+g5NAyeLSPREQAREQAREQgaoJ/E9Prmq/8hETAbd6xcCmKvfqq69a9UOEj2QrTlU9n+x3Vq9wqHJiKCSXjlVJZubZixXVIRSMHz/ersShfuh3WIpEVZSVIlZCUPfDWAkWL4OOfG644YYV+F177bV2gEn+WUF46qmnzA033JDzQWZcZRjMY5jvUesSbI899lhrlAWVSwy/BMuB8sV6IZYNWfksVpfPOh9kgOAStX4Gwyi271Hq0vTp082MGTOs8SQJccVWkkqPCIiACIhAuRDQSl0BShIBAwMBqLb51RI5w2zSpEkVUoQxFIxopBIGsDZYlbVKfh8xYkQiXIx6sGrAnrWg+/DDDxMm7IO/hfmOWqnfoebIkQZ+a4n+39N9xpz87NmzKxjrwD+cEPYwvIBD0EBVEOGM+PwOtS+sYSJ8+J0bXMKVVSWsSXIcQa4dK1+khdVR50gz59Tl2mVSl1ChxJop6pVB1UuXXozjINBhjRWDOJQP9bKQLp91vqp8ZlI/qwoz3e/BvKfzm+lvUesSq7333XefbaPsb2Sip9gcx3CwOo1qur99Fls6lR4REAEREAERSEZAQl0yKnm416xZM/Ptt9+aIUOGWMEO4YNBMTP6HE6NRUYcFgg5lNqZUWfAjBrTsGHD7MCI/WBBQRDBgZUsfmNwzb4xv3VFDKJwHhfmxDmPC8GSPz5zxlxwb1pYHAir5AEjLgyKEFb69OljV+nq16+fNBgEHFbS/MKt84jw2aJFC9OhQ4dEmOzb6tGjh101QjB2DhVAwuJcNxjhuCJsoO7ljHuQLvyxuucc8Vx++eX26ATUyqI60r5q1apKgiNxMXj1D2BRRcPQDcIzZQMjVgkxXMORBZS7S6MT/vDnPpM2BGfy5s9DmDRnUpcY5CLYYdGyTp06laJBOD7vvPOsYM3ZdZiuP+qoo2yZ33bbbVkf4k3eERqDkwXBhMRV5x17rji4O/aUM3U1OHEQTEuy72HrZ5S65OKpKu/OX9Qr+U7VNqPUJfbOoX7pDBNxNAZqmKyqZzuRggDLpBGrgWE0H6piQD/MflDqnJwIiIAIiIAIlBQBT65gBObOnetdeumlXq1atbzmzZt7kyZN8u6//36vd+/e3vTp0226Vq1a5Q0YMMCrX78+o0yvWbNm3pgxY7zZs2d7jRs39lq0aOHNnz+/Qh7Wrl3rDR061D5Tt25dr3///h7hBN2UKVO8Vq1a2fjxR7zLly8Pegv1feHChTadixcv9rp27eoRXr169bw+ffpUCnPGjBlegwYNbH7Ik/9v7NixFeJbs2aNN2rUKK9JkybW3xFHHOENGjQoaX4WLVrk3XXXXTZex2rcuHEePHCksWnTpon4Bg4caO/700O6+B7WpQqTfLh8+cP0lw3lDv9Zs2ZZ9k899ZQtd9LlniWc7t272++kfebMmYk8cD+Ky6QuEf5zzz3nBcvFxUvau3Tp4q1YscLdstdly5Z5t956qzd48OAK98N+8TNwLLimyrOfa6Z13l+Wbdq0se3R1VPS49LkL8+w+cFfpvUzVV1ycYfNu/Of7upvC37ufPbXgbB1afXq1V6vXr288ePHV4iWds29li1bevQZUR39FGUUTGOmZePiHzFihNe+fXtv5cqV7pauIiACIiACIlASBGqQypKSQpVYERCBoiHAOYKsPHJ2IHtF/e7f//633aNYqoZ3/HnR58wIsKLctm1b06lTJ7PPPvtUCISVu44dO9rD6nO9t7dCxCm+sErKSj+q4pmoi6cIVrdFQAREQAREIC8EpH6ZF8yKRATKkwAqsKhdYjjDqYNynTBhgjVbn0rttjxpKFdBAqh6N2jQwFqY5SB75hD5w5Lq008/bVV6N9988+Bjef+O+jRq7+zj5XxGOREQAREQAREoNQIS6kqtxPKUXs6AO+iggyqcY8dKTLI/BvXl5Eol78WQzr322sv861//Mu+88449woD6gdXSxx57zHTp0sUePF8M6Yy7ft5+++1J20KwfVx11VUV9lTGnY6qwit0Otlz2a5dO3PiiSeaiy++2B55wbEXZ511lt3ny0odxooKnU4OQ6esOHORMpQTAREQAREQgVIjIPXLUisxpVcEREAEREAEREAEREAEREAEfAS0UueDoY8iIAL/x955QFtRJP+/QVQMGDCHFROCgaCIqIhL0APHRX4iQZAVRBZBBQQE5awkQQkS5BlWREQwAIrCiggqKqgEH4jkqEhSBA5BEREkzP98+vfv++s774aZe+e+G17VOe/N3Jme6u5v1/R0dVVXCwKCgCAgCAgCgoAgIAgIAtmGgCh12dZiUl5BQBAQBAQBQUAQEAQEAUFAEBAELAREqbPAkFNBQBAQBAQBQUAQEAQEAUFAEBAEsg0BUeqyrcWkvIKAICAICAKCgCAgCAgCgoAgIAhYCIhSZ4GR7Cn7LjVr1kw1bdpUbd++PVl28rwgIAgIAoKAICAI+ERAvsU+AZPkgoAgkBMIiFKXgmacNGmS+uGHH1LAWVgKAoKAICAICAKCgBcE5FvsBSVJIwgIArmCQIlcqUgm1OOss85Sr7zyijrhhBNUyZIlfRXp4MGDavHixYo9vU4//XTVoEEDX8+nM/Hvv/+uli5dqhYuXKhuvfVWVaVKlZjFMXXluW+++UaddNJJqmvXrjGfCeKmyXf+/PmeyhlEnkWRx6ZNm9S6devUt99+q/f+Qp6FEkdA8EwcO3nSGwKmbyzsPtlb6fynSuZb7D83eUIQEAQEgcxAQCx1AbfD1q1b1fnnn6+uuuoqX5wPHTqk8vPz1XPPPaf4sGYT/fHHH+qLL75QeXl56sCBA3GLTl2XLFmivvvuO/3H84VBBuNx48Z5KmdhlCkX8/jll1/UhAkT1JdffqmOHj2aVBWRk2uuuUYNHz5cHTlyJClemfDwX3/9pdhw+5ZbbvFszQ8Sz0zAwEsZcq3dvdQ5nWnS1ScnU2dcLJlE/fPPPyOySfRbHJGZXBQEBAFBIAsQOKZv3759s6CcWVFErGzvv/++6tSpkzrllFN8lfm4445TN954o+JDhWWjYsWKvp63E6NYobhceeWV6thjj7VvpeT85JNP1gPvr776Sv39739XF110Ucx8qOv111+vB7YM1Pfs2aOfi/lQADfJt1KlSmrlypX6GK+cAWRZJFlceOGFqnz58nqS4o477lAnnnhiwjjQZqeddpq2rJ577rlR+RS2zEctSJwbxYsXV9SpbNmyqlq1ap7ezyDxjFO8jLmda+2eCmCDlHnwTkef7BcX6vzUU0+pp59+WvXq1UuVLl1a1atXr8B7lMy32G+ZJL0gIAgIApmCgLhfBtgS5cqV07PwAbJMiNW+ffsUs/tCgkC2I8CgrU2bNnGrkS0yX6xYMVWjRg39F7dSRThBrrV7KpoyW2Q+yLoff/zxqlu3bnri9OWXX1ZY4yJRpnyLI5VNrgkCgoAgkCoExP0yVcimiS+ujC+++KL66aef0lQCyTYWAlglR48eXcBliMA6M2fODD2Km97PP/8c+l1UTw4fPqy2bNkS0yU5SJl3HEdby1PpEvzbb7+pjRs3FtUm9VTvXGx3TxX3mChImfeYZUYkY1Lk1FNPVayZu+yyyzKiTFIIQUAQEAQyBQFR6tLYErt371Y9e/ZUF198sXa3nDhxomIw7yZcMgcNGqRd2vio4dr2/PPPhykGrCto3769whUS9xTWGuD2RnrcU3bt2uVmq77//nvVqlUr7SqKu+fIkSPDeBZ4wOMF1srdfffdmi/8f/zxR49PFkzGIJv1erfddpuuCy6qrNdiYb+buMY90lBvgrbwLDyiEbiAD+n5e/vtt6MljXid2XJwA3sCsBD45c4779R1Z2sLMDbUv39/7S5EABFDpt1wx9uxY4e+PHfuXMWMNHxZS8YaTdyNVqxYoXFFXt577z3DwtfRiyxRB8qKbLI1x7PPPqtlFLlDrhhw22S3EW7HDzzwgNq8ebOdxPc57QAvZtzBM9KMvMEuKJmH30MPPaRuv/129emnn2qZu+GGG/S6T+oN7vfdd59W+vxWyMgZLr+XXnqpevPNN6OySAWeXtodzM17ULlyZR20iULa15FNQ8gBfRZ9B8/xjhJtkPavW7duQt4CudbuYBWvX/LThyQi86a9gjp6kSWTFxMyyAPvMu8Pfcprr72mZYajkCAgCAgCgkBwCIhSFxyWvjgx09q7d28947h69Wq9/ggGn3zySRgfPqAPP/ywOuOMM3R0TAZ8KA/Lly9X48ePD6Ul4iZKAPfnzJmj2rVrp/bv369/f/zxx/r5UGKlFHmi/HXs2FH9+uuv+hkGF5QpksJkPxvrHCWKsjFoRWmtU6eOeuKJJxIaCJPPO++8o4YMGaIVC4JuTJkyRc2aNUuNGDEiTLmgzIMHD9b3SEPaF154QdeHyJzRCFzh1b17d13eFi1aREsa8TprFlE8sLzMmDFDtyMDU9rt2muvVbgIGUWdNSCsdbSJdsOy+vjjj4cuV69eXfXr10/XhcHxggULdPuAKc/D87PPPvOtgHuVpfPOO09PCBDBksA9tWvX1oo5iiRKsxtP1lIyycAfssTA7d1339XyF6qUzxPaYe/evVpJBt9IFLTMw69Lly6qRIkSWhn/6KOPVOvWrVXnzp31+lSCd/B+gYtfQs54D1F2wTQWBY2n13YH80WLFqmaNWtqZc3gfu+996pnnnlGzZ49WyGbhpAH3kX6LN63gQMHatls1KiRatu2rR7Im7Rej7nW7l76JT99iF+Z94q713ReZQl+pKXvb9iwoe5bWcvMu8Xk1LBhwxQTB0KCgCAgCAgCwSEgSl1wWPrixECIKJcMGvlQ83fPPfcoAkvYxMAW5YxoeaSBCKTCwCuS9cJ+Nto5CiUDSwK6sDie4A3MpDIQ27lzp/7oRns23nUWsjMwY5sCPuBYWQiGwoDYL+F+iFKHsnbddddpawAKR58+fdTXX38dxhNFlgEpyhBpsBywLgeKZTXCEjV16lS96D6R0PtY1LAMMgt9xRVXhILkcJ3BDHWwo5kec8wxBWAAJ6xNbsLyQRRV2h08wbVUqVLa0kN7+SWvslSmTBldJ+QOhcbIyNVXX63lbs2aNaGscSdFKWXigbIiS1iiHnvsMd/beoSYWifw4y9Z8iPzyAFbitCG4My7hzJDwBaj6CRTnkgyYPilAk+v7U4ZqB/tZ7v+Ige4uyHnhrAYoeQhk+Z9q1q1qn7fUewbN26sZdak93vMlXb30i/57UP8Yhlkej+yxOQH/RYTe/RxnPO9Y7KP/jze1jdBllt4CQKCgCBQFBBIfrRUFFAKuI5YbvjY33zzzWGz2Sgi7gEf6wawGBDJEsKahpXNduvzWzzW26FouNckMIBlQJ/MxulYlviAG2IgjHuW7XJo7sU7oqSdc845OlKgnRZ3RAaYKHYQSuPnn3+uFQ47QiJRA7GeuRVlw4uZZBSSli1bamXJXE/0eMkll2hl0jxPeyajkCAL8ICITsfgLxnyK0vgx/oVQ5HkE9daZIltB2xKtu42ryDO/cg8bWZwJ2+Uavd7GUSZIvFIBZ5+2p2BN/0S75PZQmL9+vXaBdiL/CUT6TQSHsleS2e7J9IvBd2HJIuf+3k/suR+lt9YJe3vQ6Q0ck0QEAQEAUEgMQREqUsMt6Se4mPP7Dcz3F4IKwMudygyrPdBEWGglShhjWMtzJlnnqkHrwxgzSAcl0z3mik/+bgHv/DlWiI8cfO74IILQhZKUw54MvghbDUWA5RkZn/dSirpsGxFGkQw44zbJcp1MliaMmXLMWhZwjILxigDmUyplPkg650qPP20O5bJZcuWaQs37qYLFy4soLSj6OItgKsxkXaNWyrWOyy7mULpbPdE+qVMwS1WObzKEp4LpGWCgP6fvvrDDz9Ul19+ubZ6x8pD7gkCgoAgIAj4R+D/TCr+n5UnkkQAK1Y8wj2Q4A0VKlTQboJnn322foTBVDIBSLCowYO1PrlCXvA0dX399df1mjtcg1h7xwAEF7NcplTJErgXxn6IybZNtsh80Hj6bXeCuWClZX0sVm6CvESagKpVq5Ze84jbOFZz3qV///vf+l1Ktq2CfD7d7e6nXwqy3qng5UeWmDTEBZ11uPfff7+e/PnnP/+punbtWmiW71RgIDwFAUFAEMhUBESpS0PLGOsVkcHi0bRp07T7IUE2Ilmc4j0f6T6WFQhXzlQrdVglmaFNRGFC0crPz9eWONwPbSJSJK6iWAyYEcfti2AlRLx0E/UsWbJkGH4dOnTQrp3Un5nksWPHFspgI6g2dNfRy+9UyBJWWFzcaONMc72zMSlMmbfz9XueCjz9tjvvFHvpEYwHl0sC/rjfP95rohcS2RCLd6ZSOtudtvTbL2UqjqZcfmRp1apVivW3BHgCCyFBQBAQBASB1CIg7pepxTcidxQMForj4mS7JRIkYfHixWHPEAyFIBrRlAGizsWLVsn96dOnh/gS1IPZY9asuWnevHmhUObue15+41ZqE26OWBTtqHn2/VjnhJPfsGFDWNAG0oMTyh6WAYgBJy5jKGfkZxPuP0TDZBBqkxlkgCsBYhjAEso/1YQFhLLgrmaIMiezjtHwiXcMQpbceaBYI8/uYDREuqOd0kWFKfNB1jEVeCbS7rhQEsUWa757vaSpL66iKHRE4SUQEu1Nf5ROyqR2T6RfSgY7d92T4RXtWb+yhLV36NChum82kz/ReKfrOttwYJ1mSYLdL6erPJKvICAICAKJIiBKXaLIJflc/fr11aZNm9TkyZO1YofyweCImV02pyYiI0QkOjalNmHUGTjhzvLBBx9o6wjrwdyKIIoDlizuMchi3ZgdXZGAKLh0Elaa/bhQLPnjnD3m3GvTvFaVwT11IIgLH0eUlby8PG2lw300EqHgYEmzlVuTDuWzSZMmqlu3biGerN8ZMGCAth6gGBvCFQxe7OtmlAmODDoJMmOCPFAu0mHdM0Q+bdq00Vsn4F7klyj7oUOHCiiO5IUFiz9DuCQR6AblmbYBI6yEBBthRpt2N2U0yh/pzDl8UJypm10Hwz/W0Y8swdvOE76mXJSZc4jANLQRODNo4zprsAiMgwwyoNu2bVusYsW8R91RHtyTBe6HgpJ5U0dTPzAwONDOyCr1T4bgF03mU4Gnn3Y39WKQi2JHREvj8m3ucWRSpHnz5npChb3rCF1/00036Xe9b9++ei2Vnd7vea60u9d+yU8fYrCMJ/Mmnd9jLPn0I0usncP90gSkYmsMAl/hTZHsBBoKLJOFWAO9eLzEw4DvL+tB6WuEBAFBQBDIWgQcobQhsHnzZqd169ZOqVKlnIYNGzqLFy92hg0b5owYMcJZtWqVLtehQ4ecMWPGOBUqVGAU7dSvX9+ZPXu2s2HDBqdmzZpOkyZNnG3btoXV4ejRo86UKVP0M2XKlHFGjhzpwMdNy5Ytc1q2bKnzJx357t+/353M0++dO3fqcu7evdvp06ePA79y5co5eXl5BXiuWbPGqVSpkq4PdbL/5syZE5bfkSNHnJkzZzp16tTR6apVq+ZMmDAhYn127drlDBw4UOdrsJo/f74DHhBlrFu3bii/t956S1+3y0O5+O2VovGkHqZeNk+7bWh38F+3bp3GfuzYsbrdKZd5Fj79+vXTvyn72rVrQ3Xguh/yKkt2/uRJHZGLdu3ahcpl5w1f2gQZNXK0dOlSp0OHDs6MGTOcvXv3+immTmuXwWDB0c7XZmrjmqjM221JXXkfjZxSHlMmuz3tMsQ6t3nb9YnEK2g8vba7u/zjx4933O+jSYPM9u7d2zlw4IC5pI/79u1zevTo4UycODHsutcfBmMbo2xud+qdaL8UrQ8xWHqVeZM+1tHuA93Y2zLgVZYOHz7sDB8+3MnPzw/Llv6ca82aNXP4Vvgldz9kyhrpPfLDe/r06U6XLl2cgwcP+nlM0goCgoAgkFEIFKM0WauRSsEFAUFAEBAECh0B9o/E4syekawRtumNN97Qljz2sBMqmgjgSdCxY0fVs2dPVb58+TAQsNw9+eSTerP6VK/pDss4yg+spHh4sEQgkWUCUdjKZUFAEBAECh0Bcb8sdMglQ0FAEBAEshsBXJ9xuyRwhnED5rhgwQIdtj6au3V211pK7xUBXPwrVaqkIwuzkT1zx/wRSXXcuHHapdfeA9Mr36DT4TbPcgfWb7M/o5AgIAgIAtmMgCh12dx6KSw7e8BVrlw5bB87ZuQj/TG4yyXKlrpLOSMm9wkAACAASURBVNMndf3794/4Lrjfj/bt24etqQyixJnQ7mXLllWvvvqqmjVrlt7CgHoTrXbUqFGqd+/eqmLFijrgUq71Ielsdz+yk+5ysuayU6dOqnbt2qpVq1aqePHi+q9Ro0Z6fTeWOoJUpbucbMXCO8qei8iwkCAgCAgC2YyAuF9mc+tJ2QUBQUAQEAQEAUFAEBAEBAFBoMgjIJa6Ii8CAoAgIAgIAoKAICAICAKCgCAgCGQzAqLUZXPrSdkFAUFAEBAEBAFBQBAQBAQBQaDIIyBKXZEXAQFAEBAEBAFBQBAQBAQBQUAQEASyGQFR6rK59aTsgoAgIAgIAoKAICAICAKCgCBQ5BEQpS5AEWD/nWbNmqmmTZuq7du3B8hZWAkCgoAgIAgIAoKAFwTkW+wFJUkjCAgCuYaAKHUpaNFJkyapH374IQWchaUgIAgIAoKAICAIeEFAvsVeUJI0goAgkCsIlMiVimRCPc466yz1yiuvqBNOOEGVLFnSV5EOHjyoFi9erPd2Ov3001WDBg18PZ/OxL///rtaunSpWrhwobr11ltVlSpVYhbH1JXnvvnmG3XSSSeprl27xnwmiJsm3/nz53sqZxB5FkUemzZtUuvWrVPffvut3gMKeRZKHAHBM3Hs5ElvCJi+sbD7ZG+l858qmW+x/9zkCUFAEBAEMgMBsdQF3A5bt25V559/vrrqqqt8cT506JDKz89Xzz33nOLDmk30xx9/qC+++ELl5eWpAwcOxC06dV2yZIn67rvv9B/PFwYZjMeNG+epnIVRplzM45dfflETJkxQX375pTp69GhSVUROrrnmGjV8+HB15MiRpHhlwsN//fWXYuPlW265xbM1P0g8MwEDL2XItXb3Uud0pklXn5xInTdu3Kg6d+6szjjjDHXKKaeohg0b6r7GcZwwdol+i8OYyA9BQBAQBLIIgWP69u3bN4vKm9FFXbt2rXr//fdVp06d9MfGT2GPO+44deONNyrWAmDZqFixop/Hw9KiWKG4XHnllerYY48Nu5eKHyeffLIeeH/11Vfq73//u7roootiZkNdr7/+ej2wZaC+Z88e/VzMhwK4Sb6VKlVSK1eu1Md45QwgyyLJ4sILL1Tly5fXkxR33HGHOvHEExPGgTY77bTTtGX13HPPjcqnsGU+akHi3ChevLiiTmXLllXVqlXz9H4GiWec4mXM7Vxr91QAG6TMg3c6+mS/uCxatEj169dPde/eXQ0aNEh16dJF/fnnn6pHjx7aQ8T06cl8i/2WSdILAoKAIJApCIj7ZYAtUa5cOT0LHyDLhFjt27dPMbsvJAhkOwKlS5dWbdq0iVuNbJH5YsWKqRo1aui/uJUqwglyrd1T0ZTZIvNB1Z0JQCZN77zzTsW3FmKpQ8uWLdWKFSvURx99pCdGjznmGH0fi7iQICAICAJFCQFxv8yx1saV8cUXX1Q//fRTjtUsN6qDVXL06NF6dtmuEYF1Zs6cGbqEm97PP/8c+l1UTw4fPqy2bNkS0yU5SJnHhQtreSpdgn/77TeFC5lQdARysd2j19b/nSBl3n/u6XmCPnH37t16HTbWOUPHH3+8OvPMM/U90ggJAoKAIFBUERClLo0tzweqZ8+e6uKLL9bulhMnTlSRPkoMMnE1waWNmX6Ozz//fJhiwEeuffv2ClfIp556Sgdswe2N9PXq1VO7du0qUNPvv/9etWrVSruK4u45cuTIMJ4FHvB4gbVyd999t+YL/x9//NHjkwWTMchmvd5tt92m64KLKuu1WNjvJq5xjzTUm6AtPOtea2E/By7gQ3r+3n77bft23HNmy8EN7AnAQuAXZpJZ68HWFmBsqH///goLBAFEDJl2wx1vx44d+vLcuXMVAxX4spaMNZpPP/20no0GV+TlvffeMyx8Hb3IEnWgrMgmW3M8++yzOk/kjkBADLhtstuIej/wwANq8+bNdhLf57QDvJiRB0/Wx7jJYBeUzMPvoYceUrfffrv69NNPtczdcMMNet0n9Qb3++67Tyt97rLE+23kDPewSy+9VL355ptRH0kFnl7aHczNe1C5cmUdtIlC2teRTUPIAX0WfQfP8Y4SbZD2r1u3bkLeArnW7mAVr1/y04ckIvOmvYI6epElkxcTMsgD7zLvD33Ka6+9pmWGox/CKsf7h6XbXlbA+4IVDzdlvwHK/OQvaQUBQUAQyHgEHKG0ILBv3z7nkUcecUaMGOHs379f/02YMMGpWrWq89Zbb4XKtGPHDqdx48bOqFGjdBpu7N692/nXv/7ljB49OpTOPpkzZ47Trl27UHr7njlftWqV06JFC2fhwoXOkSNHnN9++80ZMmSI061bN+fAgQMmmefjzp07nbp16zr9+vVzxo4d61C/Q4cOOePGjdPlpx6RiLryTDQCk3r16jmLFi1yjh496mzdutVp27atM2jQIM3fPEeZn3rqKX2PNKRdsmSJU716dWfx4sUmmcYEbMDI0OrVq53u3btrXM01r0fy/eKLL3Tde/XqpdsTLLk+YMAAp0uXLs7BgwdD7MDD3Tbg9Pjjj4e1O5hQ9hUrVjhbtmzR/Emzd+9eZ/r06QV4hDKIceJVljZu3OgMHTrUadKkifPEE0+EZGT58uXO3//+d+fbb78Ny2X27NnO//zP/zgrV67UsrR+/Xotnw0aNHCQi2SIMjdt2tRZs2ZNTDZByTz5VKlSxRk8eLBuw//85z9OjRo1nC+//NLZs2ePfmds2YlZqCg3kYFYMh80nl7bneLyntWsWdPhnTDEu/TMM884lMsm3s0HH3xQv5OkWbBggVOrVi1n2rRpzqRJk3QfYKf3c54r7e6lX/Lbhxgcvci8SevnGKtP9iNLpG3evLkzdepU3VfzTejTp4/TuXNn59NPPy3Qj/gpo512+/btuq/iWyYkCAgCgkBRRkAsdWlSuz/55BPtUta6dWu9LoBZyHvuuUcRWMKmvXv3qv379+ugIqSBCKRSs2bNiNYL+9lo57juEGWTgC4sjid4AzOpbdu2VTt37tQWoWjPxrvO4v0WLVrobQpKlCihrSzMohLNzi/hfvjOO++owYMHq+uuu05bA8477zzVp08f9fXXX4fxnDNnjjKL6EmD5QCrGBTLaoQlaurUqapXr14aV79lxKKGZZBZ6CuuuCIUJIfrRGWjDnY0U9Z7uAmcsDa5CcsHUVRpd7Z9ANdSpUppSw/t5Ze8ylKZMmV0nZA7oswZGbn66qu13K1ZsyaUNe6kuPs+/PDDuqzIEpaoxx57LJBZc/jxlyz5kXmzpQhtCM5EqqxevboO2GLW8iRTnkgyYPilAk+v7U4ZqB/tZ7v+IgeEiEfODWExmj17tpZJ875VrVpVv++//vqraty4sZZZk97vMVfa3Uu/5LcP8YtlkOn9yBLbmtBv1alTR9HHcc73Dg8V+vN4W994KTdWug8++EDzwrosJAgIAoJAUUYg+dFSUUYvwbrjYsnH/uabb9aDRsMGRcQ94Lvsssv0AnAiWUK46qxevTrMrc887/XIejsUDXjbxACWAX0yG6fjdsUH3BCRC3HPsl0Ozb14R5S0c845R0cKtNPijsgAE8UOQmn8/PPPtcJhR0jEHWfGjBkFFGXDCzciFBIW2qMsJUuXXHKJViYNH9ozGYUEWYAHRHQ6Bn/JkF9ZAr9TTz01lGUk+cS1Flli2wGbkq27zSuIcz8yT5sZ3Mkbpdr9XgZRpkg8UoGnn3Zn4E2/xPvEewWtX79euwB7kb9kIp1GwiPZa+ls90T6paD7kGTxcz/vR5bcz/Ibt0n7+xApjZ9rfAOYdGASKUi+fsogaQUBQUAQyBQERKlLQ0vwsedDxAy3F8LK8PLLL2tFhvU+KCIMtBIlrHGshWFxOYNX88dglvV47jVTfvJxD37hzbVEeLKh+QUXXKAH1XYZ4Mngh7DVWAzMAnq3kko6lLVIH3tmnEeMGKGV62SwtMuVDedByxKWWTBGGchkSqXMB1nvVOHpp92xTC5btkxbuLGELFy4sIDSjqKLtwBr4Ii0SzosM1jvsOxmCqWz3RPplzIFt1jl8CpLeC6QlgkC+n/66g8//FBdfvnl2uodKw8v91DokNNHHnkkYh/vhYekEQQEAUEglxD4P5NKLtUqS+qCFSse4R5I8IYKFSpoN8Gzzz5bP8JgKpkAJFjU4MEGrrlCXvA0dX399dfVCy+8oF2DODIAwcUslylVsgTuduCCTMUwW2Q+aDz9tjvBXLDSEmQId3CCvESagKpVq5YODITbOANs3Oz+/e9/63cpk2Qg3e3up1/KJNwilcWPLDFpiAs6wavuv/9+Pfnzz3/+U3Xt2jVpyzd7oi5evFgUukiNJNcEAUGgyCIgSl0amt5Yr4gMFo+mTZum3Q9Z8xXJ4hTv+Uj3jashrpypVuqwSjJDm4jChKKVn5+vLXG4H9pEpEhcRbEYMCOO2xdh4ol46SbqSVQ0G78OHTpo107qz0zy2LFjAxlsuPN2/7bL4L6X6t+pkCWssLi40caZ5npn41mYMm/n6/c8FXj6bXfeKSIMfvbZZ9rl99prr9Xuv3ZdeK+JXkhkQyzemUrpbHfa0m+/lKk4mnL5kaVVq1Yp1t8S6RUsgqJoCh0KJwq0FzfhoMoifAQBQUAQyCQExP0yDa2BgsFCcVxHbLdEgiQw+2gTodwJohFNGTh69GjE8P42D0JqT58+PXSJoB58/Fiz5qZ58+aFQpm773n5jVupTbg5YlEk0IRfIpz8hg0bwoI2wAOcUPawDEAofLiMoZyRn024/0yZMiW0PsjcM4MMcCVADANYQvmnmrCAMCDGXc0QZU5mHaPhE+8YhCy580CxRp7dwWhWrlyp28mdvrB+F6bMB1mnVOCZSLvjQrl8+XJtzXevlzT1xVUUhW78+PE6aBHvJf1ROimT2j2RfikZ7Nx1T4ZXtGf9yhLW3qFDh+q+2Uz+ROPt5TrryQky9q9//Svsm0ifyj6fTPD5JbbhwDrNkgS7X/bLR9ILAoKAIJBuBESpS1ML1K9fX23atElNnjxZK3YoHwyOmNllc2oiMkJEouNjxXoViIET7ixE/MI6wnowtyKI4oAli3sMsgjKYkdXJCAKLp3Dhg3T+3GhWPLH3lzsMedem6Yz9vCPwT11YG82Po4oK3l5edpKh/toJOJjjCXNVm5NOpTPJk2aqG7duoV4sn5nwIABCusBirEhXMHgxb5uYARxZNBJkBkze0u5SGd//MmnTZs2asiQIXpfNsPT65GyHzp0qIDiSF60EX+GcEliYILyTNuAEVZCgo0wo027mzJSTs5JZ87hg+JM3ew6GP6xjn5kCd52nvA15aLMnEMEpqGNwJlBG9dZg0VgHGSQAd22bdtiFSvmPeqO8uCeLHA/FJTMmzqa+oGBwYF2RlapfzIEv2gynwo8/bS7qReDXBQ7Iloal29zjyOTIs2bN9cTKuxd98QTT6ibbrpJv+t9+/ZNevP2XGl3r/2Snz7EtEM8mTfp/B5jyacfWWLtHO6XJiDVvffeq70j8KZIZAKN9+6NN95Q8KFPwC2YP/gzccfEWKLeAnx/WQ8KXyFBQBAQBLIWgaK8n0O6675582andevWTqlSpZyGDRvq/dSGDRum9zpjHzmIPczGjBnjVKhQgVG0U79+fb1f1IYNG/R+Uuwltm3btrCqsGfUlClT9DNlypRxRo4cGbanm0m8bNkyp2XLljp/0pk988x9P0f2I6Oc7KHHXkTwK1eunJOXl1dgvzz2AqtUqZKuD3Wy/9x7gLGH3syZM506derodNWqVXPYHwtc3LRr1y5n4MCBOl+D1fz58/WedaQ1e+mZ/Mx+gHZ5KFe8PdHsfKPxpB4mH5un3Ta0O/ivW7dOY8/+frQ75TLPwoc9zfjNPoBr167VR37H2uvMLqM59ypLdv7kSR3ZS5H99Uy57LzhS5sgo0aOli5d6nTo0MGZMWOG3lvPlMHr0S6DyTNWnW1cE5V5uy2pK/sbGjmlPKZMdnt6rY/N265PJF5B4+m13d11GT9+fNh+jvZ9ZLZ3794F9rRkL7IePXo4EydOtJN7PjcY2xhlc7tT8UT7pWh9iAHTq8yb9LGOdh/oxt7uk73K0uHDh53hw4c7+fn5YdnSn3OtWbNmvvcFjVVGykx+iRJ7f7r3FE2UlzwnCAgCgkC6EChGxlmrkUrBBQFBQBAQBAodAfaPxOLMnpGsEbYJawqWPPZVFCqaCOBJ0LFjR9WzZ09Vvnz5MBCwrD355JNq4MCBKV/THZZxlB9YSfHwYIlAIssEorCVy4KAICAIFDoC4n5Z6JBLhoKAICAIZDcCuD7jdkngDOMGzHHBggU6bH00d+vsrrWU3isCuPhXqlRJRxhmI3vmjvnDXXLcuHHapdfeA9Mr36DT4TbPcgfWb7M/o5AgIAgIAtmMgCh12dx6KSw7e8BVrlw5tIcds/HR/hjc5RJlS92lnOmTuv79+0d9H+z3pH379mFrKoMocSa0e9myZdWrr76qZs2apbcwoM5Eqx01apTq3bu3qlixog64lGt9SDrb3Y/spLucWGo7deqkateurVq1aqXYA5W/Ro0a6fXdWOoIUpXucrIVC+8oey4iw0KCgCAgCGQzAuJ+mc2tJ2UXBAQBQUAQEAQEAUFAEBAEBIEij4BY6oq8CAgAgoAgIAgIAoKAICAICAKCgCCQzQiIUpfNrSdlFwQEAUFAEBAEBAFBQBAQBASBIo+AKHVFXgQEAEFAEBAEBAFBQBAQBAQBQUAQyGYERKnL5taTsgsCgoAgIAgIAoKAICAICAKCQJFHQJS6AEWA/XeaNWummjZtqrZv3x4gZ2ElCAgCgoAgIAgIAl4QkG+xF5QkjSAgCOQaAqLUpaBFJ02apH744YcUcBaWgoAgIAgIAoKAIOAFAfkWe0FJ0ggCgkCuIFAiVyqSCfU466yz1CuvvKJOOOEEVbJkSV9FOnjwoFq8eLHe2+n0009XDRo08PV8OhP//vvvaunSpWrhwoXq1ltvVVWqVIlZHFNXnvvmm2/USSedpLp27RrzmSBumnznz5/vqZxB5FkUeWzatEmtW7dOffvtt3oPKORZKHEEBM/EsZMnvSFg+sbC7pO9lc5/qmS+xf5zkycEAUFAEMgMBMRSF3A7bN26VZ1//vnqqquu8sX50KFDKj8/Xz333HOKD2s20R9//KG++OILlZeXpw4cOBC36NR1yZIl6rvvvtN/PF8YZDAeN26cp3IWRplyMY9ffvlFTZgwQX355Zfq6NGjSVURObnmmmvU8OHD1ZEjR5LilQkP//XXX4qNl2+55RbP1vwg8cwEDLyUIdfa3Uud05kmXX2y3zo7jqO/GSxxOOWUU9TFF1+sevbsqXbv3l2AVaLf4gKM5IIgIAgIAlmCgCh1ATbU2rVr1eTJk9Vjjz2mrXV+WJ988snq0UcfVY0aNfLzWMS0KFavvfaa+vPPPyPeD/riueeeqx555BFVvnx5T6ypa/v27dUTTzyhGjdu7OmZIBKR74MPPqhuvPHGINgJjygIgC9tG4SF7qKLLlJdunRRt99+uzrmmGOi5Ki0kl6YMh+1IHFuHHvssapevXpa/i+44II4qf/3dpB4esowAxLlWrunAtIg+/l09cl+cZk3b54aNWqUnvz87bff1LJly1SJEiV0H7F3794Qu2S+xSEmciIICAKCQJYhIO6XATZYuXLl9Cx8gCwTYrVv3z7F7L6QIJDtCJQuXVq1adMmbjWyReaLFSumatSoof/iVqoIJ8i1dk9FU2aLzAdZdzxCTj31VHXOOeco3iWsde3atVMtW7bUni5M/kCZ8i0Osu7CSxAQBASBeAiIpS4eQll2H1fGF198Uf30009ZVvKiUdw9e/ao0aNHF7CiElhn5syZIRBw0/v5559Dv4vqyeHDh9WWLVtiuiQHKfO4dxE5L5UuwVgYNm7cWFSb1FO9c7HdPVXcY6IgZd5jlhmRDKv1cccdp3AXNXTaaaepyy67rECfau7LURAQBASBooKAKHVpbGnWAbAegHUBFStWVBMnTlQM5t3EIHPQoEHavZHZSdwcn3/++bCPGK6WuDTiRvPUU0/pgC0nnniins3E3WvXrl1utur7779XrVq10rOd5D9y5MgwngUe8HiBtXJ333235gv/H3/80eOTBZMxyGZ29rbbbtN14aPOei0W9ruJa9wjDTgRtIVn4RGNwAV8SM/f22+/HS1pxOvMloMb2BOAhcAvd955p6476z7A2FD//v0VFggCiBgy7Va2bFm1Y8cOfXnu3Lnq+OOP13xZS8YazaefflqtWLFC44q8vPfee4aFr6MXWaIOlBXZZGuOZ599VssockcgIAbcNtltxMz5Aw88oDZv3mwn8X1OO8CLGXfwZH2Mmwx2Qck8/B566CHt6vnpp59qmbvhhhv0Gh7qDe733XefVvrcZYn328gZboWXXnqpevPNN6M+kgo8vbQ7mJv3oHLlyjpoE4W0ryObhpAD+iz6Dp7jHSXaIO1ft27dhLwFcq3dwSpev+SnD0lE5k17BXX0IksmLyZkkAd7/Rtu0sgMR7+EJY6+iWBkhni36KfoK4QEAUFAECjKCIhSl6bWZ6a1d+/eiihdq1ev1q4jFOWTTz4JKxEf0IcfflidccYZOjomAz6Uh+XLl6vx48eH0vKRQ7ng/pw5c7RLyv79+/Xvjz/+WD8fSqyUzhPlr2PHjurXX3/VzzC4oEyRFCb72VjnKFGUjUErSmudOnX0+irqkQi98847asiQIVqxIOjGlClT1KxZs9SIESPClAvKPHjwYH2PNKR94YUXdH2IzBmNwBVe3bt31+Vt0aJFtKQRr7NGisEElpcZM2bodmRgSn2vvfZa9fLLL4cU9V69eimCtNhEu2FZffzxx0OXq1evrvr166frwuB4wYIFun3AlOfh+dlnn/lWwL3K0nnnnaeYECCCJYF7ateurRVzFEmUZjeeX331lZ5kYKIBWUIZfPfddxXylyjRDqyRQcGMNlgLWubhx/o91uigjH/00UeqdevWqnPnzurKK6/UwX14v8DFLyFnvIcou2Aai4LG02u7g/miRYtUzZo1tbJmcL/33nvVM888o2bPnq2QTUPIA+8ifRbv28CBA7Vssi64bdu2eiBv0no95lq7e+mX/PQhfmXeK+5e03mVJfiRlrW1DRs21H3rypUr9bvF5NSwYcMUEwfJEDLHhCG8cL+84oorkmEnzwoCgoAgkPUIiFKXpiZkIESUSwaNfKj5u+eee9Qdd9wRViIGtgyOiZZHGogAFAy8Ilkvwh6O8gOFkoFlp06d1PXXX6+KFy+uB2AMxHbu3KktQlEejXuZxfsMzNimgMExVhaiFhLNzi/hfohSh7J23XXXaWsACkefPn3U119/HcYTRZYBKcoQabAcYBWDYlmNmOGdOnWqQuFKJLAHFjUsg1hxGFSAKbPSXGcwQx3saKaRgn2AE9YmN2H5IIoq7Q6e4FqqVClt6SEPv+RVlsqUKaPrhNyh0BgZufrqq7XcrVmzJpQ17qQopUw8UFZkCUsUwYL8busRYmqdwI+/ZMmPzCMHbClCG4Iz7x7KDG5eRtFJpjyRZMDwSwWeXtudMlA/2s92/UUOmHxCzg1hMULJQybN+1a1alX9vqPYEwAJmU2UcqXdvfRLfvuQRDEN4jk/ssTkBzLAxB59HOd875jsoz+Pt/VNrPISCAUeTDihHPLdpM8XEgQEAUGgKCOQ/GipKKOXYN1xseRjf/PNN4fNZvNRcg/4WCuAxQBLAYQ1Dcue7dbntxist0PRgLdNDGAZ0CezcTqWJT7ghhgI42pjuxyae/GOKGksiMc10SbcERlgothBKI2ff/65VjiIxGnowgsv1NYzt6Js7jOTjELCLC/KUrJ0ySWXhA0saM9kFBJkwQxUWEfC4C8Z8itL4EdQAkOR5JOZcmSJbQdsSrbuNq8gzv3IPG1mcCdvlGr3exlEmSLxSAWeftqdgTf9Eu+T2UJi/fr12gXYi/xh4c0kSme7J9IvBd2HBN0WfmQpUt5YJe3vQ6Q0Xq4x+cC+rnwj6Mdx82eCTkgQEAQEgaKMgCh1aWh9PvbMfjPD7YWwMuByhyLDmgIUEQZaiRLWONbCnHnmmXrwygDWDMJxyXSvmfKTj3vwC1+uJcITNz/CvhsLpSkHPBn8MFuLxQAlmdlft5JKOpS1SIMIZpxxu0S5TgZLU6ZsOQYtS1hmwTgZq0xhYJdKmQ+y/KnC00+7Y5kkVDwWbtxNFy5cWEBp553EWwBXYyLtGrdUrHdYdjOF0tnuifRLmYJbrHJ4lSU8F0jLBAH9P331hx9+qC6//HJt9Y6Vh5d79O+4NbOEgMk/ZBE5FBIEBAFBoKgiIEpdGlseK1Y8YvaR4Azbtm3TboKsp3vppZd0cI94z8a6j0WNAQ8fQfcfLlXZSF7wNPV6/fXXtaskbpesvWO2N9cpVbIE7szAZzpli8wHjaffdieYC1Za1sfS7xCIItIEVK1atfSEC27jWDdxwcWFO9PWNqW73f30S5n+DvmRJSYNcUFnHS5KF94mBE5hCwL35F8y9caCTEAj5BXXXyFBQBAQBIoqAqLUpaHljfWKD1w8mjZtmnY/RPk4++yz4yX3dN+4GuLKmWrCKskMbaQ1Y/HyZnDIGqNIEUGJFImrqHGNw+0rWph46um2FHbo0EEPNIiQiYVv7NixIXezeOVK5n4kq2Ey/Pw8mwpZYnCGixttnMlUmDKfDA6pwNNvu/NOsZceVmysLAT8wf3XJt5rohcS2ZDALkwMEbyHNU70b5lC6Wx32tJvv5QpuEUrhx9ZWrVqlWL9LQGemBigfyaKr1nrHC2PaNeRMaKrEo03UjAv+niCpwgJAoKAIFBUERClLg0tTwAJFnnj4mQrGygwrBOwiWAoKETRlAE+YpE+cDYP7k+fPj10iaAezB6zHsFN8+bNC4UyrCpn6AAAIABJREFUd9/z8hu3Uptwc2SdkB01z74f65zZ1w0bNoQFbSA9OOXn5+sF+PxmwInLGANQ8rMJ9x+iYTIItcnMFIMr1gUGpERaTDVhAaEsDFAMUeZk1jEaPvGOQciSOw8Ua+TZHYyGSHe0U7qoMGU+yDqmAs9E2h0XSqLY4tLmXi9p6ourKC7MROElEBLtne5BdSa1eyL9ksE2kaO77onwiPeMX1nCejZ06FDdNyc7+YO80cfzPbHljG8o39JKlSoltDYaRRHrNEsS7H45HhZyXxAQBASBTENAlLo0tUj9+vXVpk2b1OTJk7Vih/LB4IiZXTanJiIjRCQ6NqU2YdQZOOHO8sEHH2jrCOvB3IogigOWLO7x8WPG3baUERCF/bgIBc1+XHwU+eOcPebca9O8QsTgnjoQxIWPI8pKXl6ejpxXoUKFiGxQcCJZ0kiM8tmkSRPVrVu3EE/W7wwYMEBbD1CMDeEKBi/2dTPKBEcGnbj9mCAPlIt0tvWPfNq0aaO3TsC9yC+BHZvhwtcm8sKCZVuxcEki0A3KM20DRlgJCTbCjDbtbsoIP85JZ87hj+JM3ew62PlGO/cjS/C284SnKRdl5hwiMA1tBM4M2rjOGiwC4yCDDOhw4UuUqDuDOfdkgZtfUDJv6mjqBwYGB9oZWaX+yRD8osl8KvD00+6mXgxyUeyIaBnJQ4BJkebNm+sJFfauI3T9TTfdpN/1vn37Jr15e660u9d+yU8fYtoonsybdH6PseTTjyyxdg73SxOQiq0xcMPEOyKRCTS+LzxLX2vcLPlu4kqPtfj+++8vYFH2Wnf4sB6UvkZIEBAEBIGsRcARShsCmzdvdlq3bu2UKlXKadiwobN48WJn2LBhzogRI5xVq1bpch06dMgZM2aMU6FCBUbRTv369Z3Zs2c7GzZscGrWrOk0adLE2bZtW1gdjh496kyZMkU/U6ZMGWfkyJEOfNy0bNkyp2XLljp/0pHv/v373ck8/d65c6cu5+7du50+ffo48CtXrpyTl5dXgOeaNWucSpUq6fpQJ/tvzpw5YfkdOXLEmTlzplOnTh2drlq1as6ECRMi1mfXrl3OwIEDdb4Gq/nz5zvgAVHGunXrhvJ766239HW7PJSL314pGk/qYepl87TbhnYH/3Xr1mnsx44dq9udcpln4dOvXz/9m7KvXbs2VAeu+yGvsmTnT57UEblo165dqFx23vClTZBRI0dLly51OnTo4MyYMcPZu3evn2LqtHYZDBYc7Xxtpjauicq83ZbUlffRyCnlMWWy29MuQ6xzm7ddn0i8gsbTa7u7yz9+/HjH/T6aNMhs7969nQMHDphL+rhv3z6nR48ezsSJE8Oue/1hMLYxyuZ2p96J9kvR+hCDpVeZN+ljHe0+0I29LQNeZenw4cPO8OHDnfz8/LBs6c+51qxZM4dvhV9yfw9417t06eLwLU2Gpk+frvkcPHgwGTbyrCAgCAgCaUWgGLlnrUYqBRcEBAFBQBAodATYPxKLM3tGutfQvfHGGzoQRrYGXCp0MHMwQzwJiErZs2dPVb58+bAaYrl78skn9Wb1RK9MN2ElxcODJQKJLBNId/klf0FAEBAEDALifmmQkKMgIAgIAoKAJwRwfcbtksAZxg2Y44IFC3TY+mju1p6YS6KsRwAXf9a4EVmYjeyZO+aPgCnjxo3TLr32HpjpqjCunCx3YP02+zMKCQKCgCCQzQiIUpfNrZfCsrMHXOXKlcP2sWNGPtIfg7tcomypu5QzfVLXv3//iO+C+/1o37592JrKIEqcCe1etmxZ9eqrr6pZs2bpLQyoN9FqR40apXr37q0qVqyoAy7lWh+Sznb3IzvpLidrLjt16qSjobIxOFte8NeoUSO9vhtLHUGq0l1OtmLhHWXPRWRYSBAQBASBbEZA3C+zufWk7IKAICAICAKCgCAgCAgCgoAgUOQREEtdkRcBAUAQEAQEAUFAEBAEBAFBQBAQBLIZAVHqsrn1pOyCgCAgCAgCgoAgIAgIAoKAIFDkERClrsiLgAAgCAgCgoAgIAgIAoKAICAICALZjIAoddncelJ2QUAQEAQEAUFAEBAEBAFBQBAo8giIUhegCLD/TrNmzVTTpk3V9u3bA+QsrAQBQUAQEAQEAUHACwLyLfaCkqQRBASBXENAlLoUtOikSZPUDz/8kALOwlIQEAQEAUFAEBAEvCAg32IvKEkaQUAQyBUESuRKRTKhHmeddZZ65ZVX1AknnKBKlizpq0gHDx5Uixcv1ns7nX766apBgwa+nk9n4t9//10tXbpULVy4UN16662qSpUqMYtj6spz33zzjTrppJNU165dYz4TxE2T7/z58z2VM4g8iyKPTZs2qXXr1qlvv/1W7wGFPKeCyAe5W7FiherSpYt+71KRT7p5Fhae6a6n5J8+BEzfWNh9cqpqnMy3OFVlEr6CgCAgCKQaAbHUBYzw1q1b1fnnn6+uuuoqX5wPHTqk8vPz1XPPPaf4sGYT/fHHH+qLL75QeXl56sCBA3GLTl2XLFmivvvuO/3H84VBBuNx48Z5KmdhlCkX8/jll1/UhAkT1JdffqmOHj2asiqSz3//+1+1efPmlOURNOO//vpLsfHyLbfc4tmaX1h4Bl3XZPjRP1xzzTVq+PDh6siRI8mwkmc9IJCuPtlD0aImwcWSSdQ///wzYppEv8URmclFQUAQEASyAAFR6gJspLVr16rJkyerxx57zLfV4OSTT1aPPvqoatSoUdIlQrF67bXXon7sks7AxeDcc89VjzzyiCpfvrzrTuSf1LV9+/bqiSeeUI0bN46cKAVXyffBBx9UN954Ywq4C0uDAPjStqmy0Nn5tGnTxvzMiuOxxx6r6tWrp+X/ggsu8FTmwsLTU2EKKdFFF12kra+33367OuaYY6LmWth9XdSCpOFGkHVPV5/sFzbq3KtXL1W7dm39vcG7JRIl8y2OxE+uCQKCgCCQDQiI+2WArVSuXDk9Cx8gy4RY7du3TzG7LyQICAKZhUCxYsVUjRo19F9mlSyzSlO6dGnlRWEvyn1dUaz78ccfr7p166Y6deqkXn75ZYU1LhJlyrc4UtnkmiAgCAgCqUJALHWpQjZNfHFlfPHFF9VPP/2UphJItrEQ2LNnjxo9enQBKyqBdWbOnBl6FDe9n3/+OfRbTnIHgd9++01t3LgxdyqUgpocPnxYbdmyJaYrepB9neM4Cne+wnIFTxayIOuebFkK83kmRU499VTFmrnLLrusMLOWvAQBQUAQyHgERKlLYxPt3r1b9ezZU1188cWqYsWKauLEiYrBvJsYbAwaNEi7m/BRw83x+eefD1MMWFeASyNuNE899ZRea3DiiScq0uPutWvXLjdb9f3336tWrVqpU045Rec/cuTIMJ4FHvB4gbVyd999t+YL/x9//NHjkwWTMdhivd5tt92m64IrGuu1WNjvJq5xjzTUm6AtPAuPaAQu4EN6/t5+++1oSSNeZ7Yc3MCeACwEfrnzzjt13dnaAowN9e/fX2GBIICIIdNuZcuWVTt27NCX586dq5iRhi9rilij+fTTT+uAIOCKvLz33nuGha+jF1miDpQV2WRrjmeffVbnidyxhoUBt012GyFLDzzwQNLr3GyetAt5P/7441ohjrTGinY07xL4xCsnPCPJ0kcffRSqO3KLuy51QpY+/PDDhNcIGjnDrfDSSy9Vb775pg1h2Lld96Dw9NLuyL55DypXrqyDNlEw+zqyaQg5oM+i7+I53lGiDdL+devWTchbgLyoM5YW3qNIlhjzzgTV18HvoYceUrh6fvrpp7oeN9xwg17vixwhT/fdd59W+kzd/Rzj9Ut++pBE6u6nrF7SepElwwfFHHmgTcGRd5SlAcgMRyFBQBAQBASB4BAQpS44LH1xYqa1d+/eesZx9erVOkgKDD755JMwPnxAH374YXXGGWfo6JgM+FAeli9frsaPHx9KS8RNlADuz5kzR7Vr107t379f//7444/186HESinyRPnr2LGj+vXXX/UzDC4oUySFyX421jlKFGVj0IrSWqdOHb2+inokQu+8844aMmSIViwIujFlyhQ1a9YsNWLEiDDlgjIPHjxY3yMNaV944QVdHyIkRiNwhVf37t11eVu0aBEtacTrrJFiAIrlZcaMGbodGZhS32uvvVa7CBlFnbUgBGmxiXbDsorCYqh69eqqX79+ui4MjhcsWKDbB0x5Hrejzz77zLcC7lWWzjvvPMWEABEsCdzD+hUUHBRJlGY3nl999ZWeZGCiAVli4Pbuu+9q+TN18nv8+uuv1fvvv6/lCJkGg1KlSqlFixYVmPhAzpD9u+66S5eTgTllZXLBJi+yhMLFwJlAHeDco0cPhWVt7NixatSoUVqJoTx+CTnjPSSoC5jGoqDx9NruyD741qxZU9cTuYbuvfde9cwzz6jZs2crZNMQGPMu0mfxvg0cOFBjxrrgtm3b6oG8Sev1SBn27t2rJ0dM/u5ng+7r4Ef01BIlSuhJGBT71q1bq86dO6srr7xSywJtzvvgl7z0S376EL9191veeOm9yhJ8SMva2oYNG+q+deXKlRpjotUOGzZMMXEgJAgIAoKAIBAcAqLUBYelL04MhIhyyeCBDzV/99xzj7rjjjvC+DDAQTkjWh5pIAJQMPCKNIsd9nCUHyiUDCxZl3D99der4sWL6wEYA7GdO3dqi1CUR+NeZiE7AzO2KWCQxGw7lhUGyX4J90MG4ihr1113nbYGoHD06dNHMei3eaLIMiBFGSINlgOsYlCs6IhYoqZOnaoX3ycS2AOLGhYfZqGvuOIKjSmz0lxnMEMd7GimkYI+gBNWBzdh+SCKKu0OnuCKYoPiQR5+yasslSlTRtcJuWNga2Tk6quv1nK3Zs2aUNa4k6KUMvFAWZElykewIL/beoSYKqXbkiA6tCVEfVF8wRgZs4lydujQIVROrJ4oH7aV1KssMYj/xz/+odlTB+qCLHFEwWMiJZLV2y5PvPNIMmCeSQWeXtudMqBIUVfb9Rd8cXdDzg2h+KLkIZPmfatatap+31HsaTtkNlFCjvhLlvz0dbz/bCXDu4u80eciR6eddprGJZGyeOmX/PYhiZQjqGf8yBJKMDLAxB59HOd875iEoT+Pt/VNUGUWPoKAICAIFBUEkv9qFhWkAqwnlhs+9jfffHPY4JzBo3vAx7oBZo4ZbEJY07Cy2QNWv0VjvR2KhntNAgMZBvTJbJyOZYkPuCEGRLja2C6H5l68I0raOeecoxik24Q7IgNMFDsIpfHzzz/XCgeROA1deOGF2nrmVpTNfWaSUUhatmyplSVzPdHjJZdcohUA8zztmczAFFmAB3TcccfpwabhncjRryyBH+tXDEWSTyx4yBLh521Ktu4ob7j22QF/GPyiPNplIk8v5fQqS6YObp5cN+2b6GSK4R3rmAo8/bQ7A2/6Jd4n4+a6fv167QIM/vEIC28mkZ++jnfVvG/UgckUd3/sp26J9EtGxkw+yb5Hhk9QRz+yFClPrJL29yFSGrkmCAgCgoAgkBgCotQlhltST/GxZ/bbWCHiMWO2GVcwFBnWfaCIMNBKlLDGMWA+88wz9SCGgYMZPOCS6V4z5Scf9yAIvlxLhCdufoR9NxZKUw54MvghbDUWA5RkZn/dSirpsGxFGkQw44zbJcp1MliaMmXLMWhZwmoGxslYZSJhx+QA1uhatWrpNU5YyGgzFItEFGWvshSpLOYaebOfl215NfeCOqYKTz/tjoVq2bJl2sKN2+HChQsLKO28k7QPrsYo3sY9Eesdlt1MoVT2dfHqmEi/FI9nJtz3KktMzJCWCQL6f/pq1qVefvnl2vqZCXWRMggCgoAgkEsIiFKXxtbEihWPcA9kkf62bdu0myDr6V566SUd3CPes7HuM2hmwMNgzP2HS1U2khc8Tb1ef/117SrJOjfW3mG1y3VKlSyBOzPwQRKKePPmzfU6Utbo4a6MBYl1achrLlPQePptd4K5YKlkfSz9Du6mkSagULhR7nAbR9HGiooLN4P5TKJ093V++qVMwi1SWfzIEpOGuKCzDhePC7xNCJzCem/35F+kvOSaICAICAKCgD8E/s9Pzt9zkjoJBIz1ig9cPJo2bZp2P0T5iGRxivd8pPtYViBcOQngkErCKskMLWty/BKDw/z8fG2Jw/3QJiJF4irKoJIZcdy+CFZClEI3UU/Wd9n4sQaLgQb1ZyaZQBhdu3ZN+WDDLoO7nKn+nQpZYnCGixttnArXO2MRwipEgAUC+TA4pO39kFdZisWT9W5QKt+ZVODpt93BnL30CMaDdZKAP+73j/ea6IVENsTinalUmH2dGwPa0m+/5OaRab/9yNKqVasU628J8CRKXKa1pJRHEBAEchEBsdSloVVRMFgojouT7ZbIoHHx4sVhJWL9DkE0oikDRJ2LF62S+9OnTw/xJagHs8esM3LTvHnzQqHM3fe8/Mat1CZc5lgnZEfNs+/HOies+IYNG8KCNpAenFD2WIAPMeDEZQzljPxswv2HaJgMQm0ygwxwxbrAAJZQ/qkmLCCUxbY2UeZk1jF6LXMQsuTOC+UKeXYHoyHSnVGC3M/E+42SjmzaGPEMgViImIeF2S95lSXDFzm2302uI3NYrP72t7+ZZIEfU4FnIu2OCyVRbHGvdK+XNJXGVRSFDtdYghbR3vRH6aTC7Ovi1TORfikez1j33XWPlTbRe35lCWvv0KFDdd9sJn8SzTtVz7ENB9ZpliS4+5xU5Sl8BQFBQBBIBQKi1KUCVQ8869evrzZt2qQmT56sB48oHwyOmNllc2oiMkJEomNTahNOm4ET7iwffPCBto6wHsytCKI4YMniHoMs1o3Z0RUJiMK+TISVJvw7g1f+OCcMvHttmofq6CQM7qkDQVz4OKKs5OXlaStdhQoVIrJBwcGS5h5Akxjls0mTJqpbt24hnqzfGTBggLYeoBgbwhUMXuzrZpQJjgw6seyYIA+Ui3QoDobIp02bNnrrBNyL/BJlZ60VfG0iLyxY/BnCJYlANyjPtA0YYSVknRYz2rS7KSP8OCedOYcPCgd1s+tg+Mc6+pEleNt5wteUizJzDhGYhjYCZwZtXGcNFoFxkEEGdLjw+SHyZesGFAq2E4DAgG0NsMbaSpXXcvqRJfJjwoV1rNSVvHkH//Of/6j7778/6fWD1C+azKcCTz/tbtqJQS6KHREtzz77bHM5dGRSBPdYFBf2riN0/U033aTf9b59+ya9iTeYozS6J4lCBfj/J0H1dUa2jVzTRkb+eb9pL2TBL3ntl/z0IaYM8epu0vk9xpJPP7LE2jncL01AKrbGwDsCb4pkJ9BQYJksxBroxeMlHgZ8f1kPiswJCQKCgCCQtQg4QmlDYPPmzU7r1q2dUqVKOQ0bNnQWL17sDBs2zBkxYoSzatUqXa5Dhw45Y8aMcSpUqMAo2qlfv74ze/ZsZ8OGDU7NmjWdJk2aONu2bQurw9GjR50pU6boZ8qUKeOMHDnSgY+bli1b5rRs2VLnTzry3b9/vzuZp987d+7U5dy9e7fTp08fB37lypVz8vLyCvBcs2aNU6lSJV0f6mT/zZkzJyy/I0eOODNnznTq1Kmj01WrVs2ZMGFCxPrs2rXLGThwoM7XYDV//nwHPCDKWLdu3VB+b731lr5ul4dy8dsrReNJPUy9bJ5229Du4L9u3TqN/dixY3W7Uy7zLHz69eunf1P2tWvXhurAdT/kVZbs/MmTOiIX7dq1C5XLzhu+tAkyauRo6dKlTocOHZwZM2Y4e/fu9VNM3V7IDXJ01113afksXbq007ZtW4d3xpDfcnqVJTCnrnPnztXvJW3B+7lo0aKQLJkyeD265cS0ry0bhlfQeHptd5O/OY4fP95xv4/mHjLbu3dv58CBA+aSPu7bt8/p0aOHM3HixLDrXn/YbWow4mjLm83Lfp8S7evstqHd6YdN/0R5TJkitZVdlmjnifZL0foQk4/Xupv0sY52H2jjzrktA15l6fDhw87w4cOd/Pz8sGx5B7nWrFkzh2+FX3L3Q6asibaNyX/69OlOly5dnIMHD5pLchQEBAFBIOsQKEaJs1YjlYILAoKAIBAwAliesJqylyPry4QKIsD+kVic2TOSNcI2vfHGG3oNVbYGXLLrIueJIYAnQceOHRVBjsqXLx/GBMvdk08+qTerT+X61LBMY/zASoqHB0sEElkmEIO13BIEBAFBoFAREPfLQoVbMhMEBIFMRwA3O9xpGewJRUYA12eUXwJnGDdgjrjIErY+mrt1ZG5yNdcQwMW/UqVKOrIwG9kzd8wfkVRxrcal173fZDow4D1nuQNrbomuKyQICAKCQDYjIEpdNrdeCsvOHnAEpWAWPt4fg7tcomypu5QzeKnr37+/YrP6MWPG6AiQYByJSBfvveB++/btw9ZURuLl91omtHvZsmXVq6++qmbNmqW3MKCuRBgdNWqUjlBasWJFHXAp1/qQdLa7HzlJdzlZc9mpUydVu3Zt1apVK73lBdteNGrUSK/vxlJHkKp0l5OtWHhHia6LDAsJAoKAIJDNCIj7ZTa3npRdEBAEBAFBQBAQBAQBQUAQEASKPAJiqSvyIiAACAKCgCAgCAgCgoAgIAgIAoJANiMgSl02t56UXRAQBAQBQUAQEAQEAUFAEBAEijwCotQVeREQAAQBQUAQEAQEAUFAEBAEBAFBIJsREKUum1tPyi4ICAKCgCAgCAgCgoAgIAgIAkUeAVHqAhQB9t9p1qyZatq0qdq+fXuAnIWVICAICAKCgCAgCHhBQL7FXlCSNIKAIJBrCIhSl4IWnTRpkvrhhx9SwFlYCgKCgCAgCAgCgoAXBORb7AUlSSMICAK5gkCJXKlIJtTjrLPOUq+88oo64YQTVMmSJX0V6eDBg2rx4sV6b6fTTz9dNWjQwNfz6Uz8+++/q6VLl6qFCxeqW2+9VVWpUiVmcUxdee6bb75RJ510kuratWvMZ4K4afKdP3++p3IGkWdR5LFp0ya1bt069e233+o9oJDnVBD5IHcrVqxQXbp00e9dKvJJN8/CwjPd9ZT804eA6RsLu09OVY2T+RanqkzCVxAQBASBVCMglrqAEd66das6//zz1VVXXeWL86FDh1R+fr567rnnFB/WbKI//vhDffHFFyovL08dOHAgbtGp65IlS9R3332n/3i+MMhgPG7cOE/lLIwy5WIev/zyi5owYYL68ssv1dGjR1NWRfL573//qzZv3pyyPIJm/Ndffyk2Xr7llls8W/MLC8+g65oMP/qHa665Rg0fPlwdOXIkGVbyrAcE0tUneyhagSQbN25UnTt3VmeccYY65ZRTVMOGDXVf4zhOWNpEv8VhTOSHICAICAJZhIAodQE21tq1a9XkyZPVY4895ttqcPLJJ6tHH31UNWrUKOkSoVi99tpr6s8//0yalxcG5557rnrkkUdU+fLlvSRX1LV9+/bqiSeeUI0bN/b0TBCJyPfBBx9UN954YxDshEcUBMCXtk2Vhc5kSz5t2rQxP7PieOyxx6p69epp+b/gggs8lbmw8PRUmEJKdNFFF2nr6+23366OOeaYqLkWdl8XtSBpuBFk3dPVJ/uFbdGiRapHjx7qoYceUqybY+36HXfcodq2bavmzZsXYpfMtzjERE4EAUFAEMgyBMT9MsAGK1eunJ6FD5BlQqz27dunmN0XEgQEgcxCoFixYqpGjRr6L7NKllmlKV26tCeFvSj3dUWt7lhs33//fXXnnXcqvrUQSx1atmypXbA/+ugjPWHHJECmfIsz662S0ggCgkCuIyCWuhxrYVwZX3zxRfXTTz/lWM1yozp79uxRo0ePLmBFJbDOzJkzQ5XETe/nn38O/ZaT3EHgt99+U7iQCUVH4PDhw2rLli0xXdGD7Otw3cPyU1iu4NFr7u1OkHX3lmP6U9En7t69W6/Dtr1Qjj/+eHXmmWfqe6QREgQEAUGgqCIgSl0aW54PVM+ePdXFF1+sKlasqCZOnKgifZQYbAwaNEi7NzLTj5vj888/H6YY8JHDpRE3mqeeekoHbDnxxBMV6XH32rVrV4Gafv/996pVq1Z6XQL5jxw5MoxngQc8XmCt3N133635wv/HH3/0+GTBZAy2WK9322236brgisZ6LRb2u4lr3CMN9SZoC8+611rYz4EL+JCev7ffftu+Hfec2XJwA3sCsBD4hZlk1nqwtQUYG+rfv7/CAkEAEUOm3cqWLat27NihL8+dO1cxUIEva4pYo/n000/r2WhwRV7ee+89w8LX0YssUQfKimzi3vTss8/qPJE7AgEx4LbJbiPq/cADDyS9zs3mSbuQ9+OPP64V4khrrGhH8y6BT7xywjOSLDHbb+qO3OKuS52QpQ8//DDhNYJGznArvPTSS9Wbb75pQxh2btc9KDy9tDuyb96DypUr66BNFMy+jmwaQg7os+g7eI53lGiDtH/dunUT8hYgL+qMpYX3iHVRbjLvTFB9Hfxw58PV89NPP9X1uOGGG/R6X+QIebrvvvu00ucui5ff8folP31IInX3UkY/abzIkuGHYo480KbgyDvK0gBkhqMfwipHO2Dpxo3ZEO8LfcKFF17oO0CZ4SFHQUAQEARyAQFR6tLUisy09u7dWxGla/Xq1TpICkX55JNPwkrEB/Thhx/Wi8KJjskHDOVh+fLlavz48aG0fPBQArg/Z84c1a5dO7V//379++OPP9bPhxIrpfNE+evYsaP69ddf9TMMLihTJIXJfjbWOUoUZWPQitJap04dvb6KeiRC77zzjhoyZIhWLAi6MWXKFDVr1iw1YsSIMOWCMg8ePFjfIw1pX3jhBV0fIiRGIxbbw6t79+66vC1atIiWNOJ1BhcMQLG8zJgxQ7cjA1Pqe+2116qXX345pKj36tVLEaTFJtoNyyoKi6Hq1aurfv366bowOF6wYIFuHzDleXh+9tmqKnaVAAAgAElEQVRnvhVwr7J03nnnKSYEiGBJ4J7atWtrxRxFEqXZjedXX32lJxmYaECWGLi9++67Wv5Mnfwev/76a+1qRZ2RaTAoVaqUYk2Ne+IDOUP277rrLl1OBuaUlckFm7zIEgoXA2cCdYAz63ewrI0dO1aNGjVKKzGUxy8hZ7yHBHUB01gUNJ5e2x3ZB9+aNWvqehoXt3vvvVc988wzavbs2QrZNATGvIv0WbxvAwcO1JixLpg1Tgzk/RJl2Lt3r54cMfm7eQTd18GP6KklSpTQkzAo9q1bt9bBOK688kotC7Q574Nf8tIv+elD/Nbdb3njpfcqS/AhLWtrCWTCO7py5UqNMdFqhw0bppg48EvIH5NltJUh8lm1alVocs5cl6MgIAgIAkUNAVHq0tTiDISIcsnggQ81f/fcc49e9G0XiQEOyhnR8kgDEYCCgVekWWz72WjnKJQMLDt16qSuv/56Vbx4cT0AYyC2c+dObRGK9my86yzeZ2DGNgV8eJltZxaVQbJfwv2QgTjK2nXXXaetASgcffr0UQz6bZ4osgxIUYZIg+UAqxgUKzoilqipU6cqFK5EAntgUcPiwyz0FVdcoTFlMMt1BjPUwY5mGinoAzhhdXATlg+iqNLu4AmuKDYoHokMmL3KUpkyZXSdkDuizBkZufrqq7XcrVmzJlRU3ElRSpl4oKzIEuUjWJDfbT1CTJXSbUkQHdoSor4ovmCMjNlEOTt06BAqJ1ZPBn+2ldSrLDGI/8c//qHZUwfqgixxRMFjIiWS1dsuT7zzSDJgnkkFnl7bnTKgSFFX2/UXfJl8Qs4Nofii5CGT5n2rWrWqft9R7Gk7ZDZRQo74S5b89HW8/2wlw7uLvNHnIkennXaaxiWRsnjpl/z2IYmUI6hn/MgSSjAywMQefRznfO9Q8OjP421946XMKNsffPCB5pWIkuglD0kjCAgCgkC2IJD8VzNbappB5cTSwMf+5ptvDhucM3h0D/guu+wyxcwxg00IaxqWPXvA6rdqrLdD0YC3TQxkGNAns3E6liV7FpUBEa42tsuhnWesc5S0c845RzFItwl3RAaYKHYQSuPnn3+uFQ4icRrCHQfrGdHRIhEzvCgkLLRHWUqWLrnkEq0AGD60ZzIDU2QBHtBxxx2nB5uGdyJHv7IEfqeeemooq0jyiYsiskT4eZuSrTvKG659dsAfBr8oj3aZyNNLOb3KkqmDmyfXTfsmOplieMc6pgJPP+3OwJt+iffJuLmuX79euwCDfzzCwptJ5Kev41017xt1YDLF3R/7qVsi/ZKRMZNPsu+R4RPU0Y8sRcoTq6T9fYiUxs81vgFMOjABEyRfP2WQtIKAICAIZAoCotSloSX42PMhMlaIeEVgthlXMBQZ1n2giDDQSpSwxjFgZnE5gwbzx6AGl0z3mik/+bgHQfDmWiI8cfMj7LuxUJpywJPBD2GrsRigJDP761ZSSYeyFuljz4wzbpco18lgacqULcegZQmrGRgnY5WJhB2TA1ija9Wqpdc4YSGjzVAsElGUvcpSpLKYa+TNfl625dXcC+qYKjz9tDsWqmXLlmkLN5aQhQsXFlDaeSdpH1yNUbyNeyLWOyy7mUKp7Ovi1TGRfikez0y471WWmJghLRME9P/01axLvfzyy7X1M9m6oNAhp2ynE6mPT5a/PC8ICAKCQLYhIEpdGlsMK1Y8wj2QxeHbtm3TboKsp3vppZf0+oF4z8a6z6CZAQ+DMfcfLlXZSF7wNPV6/fXXtaskbpesvcNql+uUKlkCdztwQRA4Mkhr3ry5Yh0pa/RwV8aCxLo05DWXKWg8/bY7wVywVLI+ln4Hd9NIE1Ao3Ch3uI2jaGNFxYWbwXwmUbr7Oj/9UibhFqksfmSJSUNc0FmHi8cF3iYETmG9t3vyL1Jesa6x7pQ1swSoEoUuFlJyTxAQBIoSAqLUpaG1jfWKD1w8mjZtmnY/RPk4++yz4yX3dN+4GuLKmWrCKskMbaQ1Y/HyZnDIGiN3YAyeI1IkrqLGRQq3r2hh4qmn21LIGiwGGkQ1xMJHIAzjbhavXMncT+cAJBWyxOAMFzfaOBVkLEIEiMG6TLCSWGsko5XBqyxFe57ryCJE0JNUUSrw9NvuYE6EQazYWFkI+IP7r028K0QvJLIhA2wUbYL3EFSH/i1TqDD7OnedaUu//ZKbR6b99iNLBC9h/S3BjpgYoH8miq9Z65xo3ZA3JnvcFjoUzmSCfCVaHnlOEBAEBIFMQUCUujS0BAEkWCiO64itbDBo5GNlE+t3UIiiKQNEnYv3IeP+9OnTQ2wJ6sHsMeuM3DRv3rxQKHP3PS+/cSu1CZc51gkRcMAvEVZ8w4YNYUEb4AFO+fn5egE+vxlw4jLGAJT8bML9h2iYboXNzBSDK9YFBqSE8k81YQGhLLa1iTIns47Ra5mDkCV3XijWyLNb0SLSnVGC3M/E+40Sj2zaGPEMgVgIhoCF2S95lSXDFzm2302uI3NYrP72t7+ZZIEfU4FnIu2OCyVRbHGvdK+XNJXGVRQXZlxjCVpEe9MfpZMKs6+LV89E+qV4PGPdd9c9VtpE7/mVJay9Q4cO1X1zEJM/rCfHav+vf/0r7JtIn8o+n5EmAOPVlW04sE4zaeTuc+I9K/cFAUFAEMgkBESpS1Nr1K9fX23atElNnjxZDx5RPhgcMbPL5tREZISIRMfHyoTTZuCEOwsRv7COsB7MrQiiOGDJ4h6DLGbcbUsZAVHYl4mw0oR/Z/DKH+e4tLjXpnmFiME9dSCICx9HlJW8vDwdOa9ChQoR2fAxjmRJIzHKZ5MmTVS3bt1CPFm/M2DAAG09QDE2hCsYvNjXzSgTHBl04vZjgjxQLtLZH3/yadOmjd46gdlevwR2rLWCr03kRRvZVixckhiYoDzTNmCElZB1Wsxo0+6mjPDjnHTmHP4oHNTNroOdb7RzP7IEbztPeJpyUWbOIQLT0EbgzKCN66zBIjAOMsiADhc+P0S+WOZQKNhOAAIDtjVgtt9WqryW048skR8TLqxjpa7kzTv4n//8R91///1Jrx+kftFkPhV4+ml3004MclHsiGgZyUOASRHcY1Fc2LuO0PU33XSTftf79u2b9CbeYI7S6J4kMuUzx6D6OiPbRq5pIyP/vN+0F7Lgl7z2S376EFOGeHU36fweY8mnH1li7RzulyYgFVtjGO+IRCbQwP+NN95Q8EE2sP7xB38m7pgYSzRQD99f1oPCV0gQEAQEgaxFwBFKGwKbN292Wrdu7ZQqVcpp2LChs3jxYmfYsGHOiBEjnFWrVulyHTp0yBkzZoxToUIFRtFO/fr1ndmzZzsbNmxwatas6TRp0sTZtm1bWB2OHj3qTJkyRT9TpkwZZ+TIkQ583LRs2TKnZcuWOn/Ske/+/fvdyTz93rlzpy7n7t27nT59+jjwK1eunJOXl1eA55o1a5xKlSrp+lAn+2/OnDlh+R05csSZOXOmU6dOHZ2uWrVqzoQJEyLWZ9euXc7AgQN1vgar+fPnO+ABUca6deuG8nvrrbf0dbs8lIvfXikaT+ph6mXztNuGdgf/devWaezHjh2r251ymWfh069fP/2bsq9duzZUB677Ia+yZOdPntQRuWjXrl2oXHbe8KVNkFEjR0uXLnU6dOjgzJgxw9m7d6+fYur2Qm6Qo7vuukvLZ+nSpZ22bds6vDOG/JbTqyyBOXWdO3eufi9pC97PRYsWhWTJlMHr0S0npn1t2TC8gsbTa7ub/M1x/Pjxjvt9NPeQ2d69ezsHDhwwl/Rx3759To8ePZyJEyeGXff6w25TgxFHW95sXvb7lGhfZ7cN7U4/bPonymPKFKmt7LJEO0+0X4rWh5h8vNbdpI91tPtAG3fObRnwKkuHDx92hg8f7uTn54dlyzvItWbNmjl8K/xQrDJSTvJLlKZPn+506dLFOXjwYKIs5DlBQBAQBNKOQDFKkLUaqRRcEBAEBIGAEcDyhNWUvRxZXyZUEAH2j8TizJ6R7jV0WFOw5GVrwKWCtZUrfhHAk6Bjx446yFH58uXDHsey9uSTT+rN6lO5PjUs0xg/sJLi4cESgUSWCcRgLbcEAUFAEChUBMT9slDhlswEAUEg0xHAzQt3WgZ7QpERwPUZ5ZfAGcYNmCMusoStj+ZuHZmbXM01BHDxr1Spko4szEb2zB3zh7skrtW49Lr3m0wHBrznLHdgzS3RdYUEAUFAEMhmBESpy+bWS2HZ2QOOoBTMwsf7Y3CXS5QtdZdyBi91/fv315vVjxkzRkeABONIRLp47wX3Cblur6mMxMvvtUxo97Jly6pXX31VzZo1S29hQF2JMEp00t69e6uKFSvqgEu51oeks939yEm6y4mltlOnTjoaaqtWrfSWF2x70ahRI72+G0sdQarSXU62YuEdZc9FZFhIEBAEBIFsRkDcL7O59aTsgoAgIAgIAoKAICAICAKCgCBQ5BEQS12RFwEBQBAQBAQBQUAQEAQEAUFAEBAEshkBUeqyufWk7IKAICAICAKCgCAgCAgCgoAgUOQREKWuyIuAACAICAKCgCAgCAgCgoAgIAgIAtmMgCh12dx6UnZBQBAQBAQBQUAQEAQEAUFAECjyCIhSF6AIsP9Os2bNVNOmTdX27dsD5CysBAFBQBAQBAQBQcALAvIt9oKSpBEEBIFcQ0CUuhS06KRJk9QPP/yQAs7CUhAQBAQBQUAQEAS8ICDfYi8oSRpBQBDIFQRK5EpFMqEeZ511lnrllVfUCSecoEqWLOmrSAcPHlSLFy/WezudfvrpqkGDBr6eT2fi33//XS1dulQtXLhQ3XrrrapKlSoxi2PqynPffPONOumkk1TXrl1jPhPETZPv/PnzPZUziDyLIo9NmzapdevWqW+//VbvAYU8p4LIB7lbsWKF6tKli37vUpFPunkWFp7prqfknz4ETN9Y2H1yqmqczLc4VWUSvoKAICAIpBoBsdQFjPDWrVvV+eefr6666ipfnA8dOqTy8/PVc889p/iwZhP98ccf6osvvlB5eXnqwIEDcYtOXZcsWaK+++47/cfzhUEG43HjxnkqZ2GUKRfz+OWXX9SECRPUl19+qY4ePZqyKpLPf//7X7V58+aU5RE047/++kux8fItt9zi2ZpfWHgGXddk+NE/XHPNNWr48OHqyJEjybCSZz0gkK4+2UPRwpI4jqO/GSxxOOWUU9TFF1+sevbsqXbv3h2Wjh+JfosLMJILgoAgIAhkCQKi1AXYUGvXrlWTJ09Wjz32mG+rwcknn6weffRR1ahRo6RLhGL12muvqT///DNpXl4YnHvuueqRRx5R5cuX95JcUdf27durJ554QjVu3NjTM0EkIt8HH3xQ3XjjjUGwEx5REABf2jZVFjqTLfm0adPG/MyK47HHHqvq1aun5f+CCy7wVObCwtNTYQop0UUXXaStr7fffrs65phjouZa2H1d1IKk4UaQdU9Xn+wXtnnz5qlRo0bpyc/ffvtNLVu2TJUoUULLyt69e0PskvkWh5jIiSAgCAgCWYaAKHUBNli5cuX0LHzp0qUD5Oqf1b59+xSz+0KCgCCQWQgUK1ZM1ahRQ/3zn//0PfGTWTVJbWnoQ1HYK1SoEDOjotzXFcW64xFy6qmnqnPOOUfxLmGta9eunbbK4eliKFO+xaY8chQEBAFBoDAQEKWuMFAuxDxwZXzxxRfVTz/9VIi5SlZeEdizZ48aPXp0ASsqgXVmzpwZYoOb3s8//xz6LSe5gwAWho0bN+ZOhVJQk8OHD6stW7bEdEUPsq/DrY+IiYXlCp4sZEHWPdmyFObzWK2PO+44hbuoodNOO01ddtllBfpUc1+OgoAgIAgUFQREqUtjS7MOgPUArAuoWLGimjhxomIw7yYGG4MGDdLujcxO4ub4/PPPh33EcLXEpRE3mqeeekoHbDnxxBP1bCbuXrt27XKzVd9//71q1aqVnu0k/5EjR4bxLPCAxwuslbv77rs1X/j/+OOPHp8smIzBFrOzt912m64LH3XWa7Gw301c4x5pwImgLTwLj2gELuBDev7efvvtaEkjXme2HNzAngAsBH658847dd1Z9wHGhvr376+wQBBAxJBpt7Jly6odO3boy3PnzlXHH3+85suaItZoPv300zogCLgiL++9955h4evoRZaoA2VFNtma49lnn9V5IncEAmLAbZPdRsycP/DAA0mvc7N5Gpl//PHHtUIcaY0V7WjeJfCJV054RpKljz76KFR35BZ3XeqELH344YcJrxE0coZb4aWXXqrefPNNG8Kwc7vuQeHppd2RffMeVK5cWQdtomD2dWTTEHJAn0XfwXO8o0QbpP3r1q2bkLcAeVFnLC28R6yLcpN5Z4Lq6+D30EMPKVw9P/30U12PG264Qa/dQo6Qp/vuu08rfe6yePkdr1/y04ckUncvZfSTxossGX4o5sgDbQqOvKMsDUBmOPol2oi+iWBkhni36KeQGSFBQBAQBIoyAqLUpan1mWnt3bu3IkrX6tWrdZAUivLJJ5+ElYgP6MMPP6zOOOMMHR2TAR/Kw/Lly9X48eNDafnIoVxwf86cOdolZf/+/fr3xx9/rJ8PJVZK54ny17FjR/Xrr7/qZxhcUKZICpP9bKxzlCjKxqAVpbVOnTp6fRX1SITeeecdNWTIEK1YEHRjypQpatasWWrEiBFhygVlHjx4sL5HGtK+8MILuj5ESIxG4Aqv7t276/K2aNEiWtKI11kjxWACy8uMGTN0OzIwpb7XXnutevnll0OKeq9evRRBWmyi3bCsorAYql69uurXr5+uC4PjBQsW6PYBU56H52effeZbAfcqS+edd55iQoAIlgTuqV27tlbMUSRRmt14fvXVV3qSgYkGZImB27vvvquQv0Tp66+/Vu+//76WI2QaDEqVKqUWLVoUwtPwRs6Q/bvuukuXk4E5ZWVywSYvsoTCxcCZQB3g3KNHD4VlbezYsXotD0oM5fFLyBnvIUFdwDQWBY2n13ZH9sG3Zs2aWlkzg+R7771XPfPMM2r27NkK2TQExryL9Fm8bwMHDtSYsS64bdu2eiBv0no9UgbWRjGxYPJ3Pxt0Xwc/oqeyNotJGBT71q1bq86dO6srr7xSywJtzvvgl7z0S376EL9191veeOm9yhJ8SMva2oYNG+q+deXKlRpjotUOGzZMMXGQDCFzTLzAq2XLluqKK65Ihp08KwgIAoJA1iMgSl2ampCBEFEuGTzwoebvnnvuUXfccUdYiRjgMDgmWh5pIAJQMPCKNIsd9nCUHyiUDCw7deqkrr/+elW8eHE9AGMgtnPnTm0RivJo3Mss3mdgxjYFDJKYbceywiDZL+F+yEAcZe26667T1gAUjj59+igG/TZPFFkGpChDpMFyYNY2xoqOyAzv1KlTFQpXIoE9sKhh8WEWmkEFmDIrzXUGM9TBjmYaKegDOGF1cBOWD6Ko0u7gCa4oNige5OGXvMpSmTJldJ2QOwa2RkauvvpqLXdr1qwJZY07KUopEw+UFVmifAQL8rutR4ipUrotCaJDW0LUF8UXjJExmyhnhw4dQuXE6onyYVtJvcoSg/h//OMfmj11oC7IEkcUPCZSsAwkQ5FkwPBLBZ5e250yoEhRV9v1F3yZfELODaH4ouQhk+Z9q1q1qn7fUexpO2Q2UUKO+EuW/PR1vP9sJcO7i7zR5yJHuPdFUzDjlc9Lv+S3D4mXZyrv+5EllGBkgIk9+jjO+d4xCUN/Hm/rm1j1IBAKPJhwQjnku8l7KiQICAKCQFFGIPmvZlFGL8G642LJx/7mm28OG5zzUXIP+FgrwMwxg00IaxqWPXvA6rcYrLdD0YC3TQxkGNAns3E6liU+4IYYEOFqY7scmnvxjihpLIhnkG4T7ogMMFHsIJTGzz//XCscROI0dOGFF2rrmVtRNveZSUYhYZYXZSlZuuSSS8IGFrRnMgNTZMEMVFhHwuAvGfIrS+BHUAJDkeSTmXJkifDzNiVbd5Q3rGJ2wB/qj/Jol4k8vZTTqyyZOrh5ct20b6KTKYZ3rGMq8PTT7gy86Zd4n4yb6/r167ULsBf5w8KbSeSnr+NdNe8bdWAyxd0f+6lbIv2SkTGTT7LvkeET1NGPLEXKE6uk/X2IlMbLNZRs9nXlvaYfx82fCTohQUAQEASKMgKi1KWh9fnYM/ttrBDxisBsM65gKDKsKUARYaCVKGGNY8B85pln6kEMAwczeMAl071myk8+7kEQfLmWCE/c/Aj7biyUphzwZPDDbC0WA5RkZn/dSirpUNYiDSKYccbtEuU6GSxNmbLlGLQsYTUD42SsMpGwY3IAa3StWrX0GicsZLQZikUiirJXWYpUFnONvAnQYFtezb2gjqnC00+7Y6EiVDwWbtwOFy5cWEBp552kfXA1RvE27olY77DsZgqlsq+LV8dE+qV4PDPhvldZYmKGtEwQ0P/TV7Mu9fLLL9fWz2TrQv+OWzNLCJj8QxYTcY1OthzyvCAgCAgCmYKAKHVpbAmsWPGI2UcW6W/btk27CbKe7qWXXtLBPeI9G+s+g2YGPHwE3X+4VGUjecHT1Ov111/XrpK4XbL2jtneXKdUyRK4MwMfJKGIN2/eXM/Gs0YPd2UsSKxLy/WBW9B4+m13grlgqWR9LP0O7qaRJqBQuFHucBtH0caKigs3g/lMonT3dX76pUzCLVJZ/MgSk4a4oLMOF6ULbxMCp7AFgXvyL1JeXq8x2UJgG+QV118hQUAQEASKKgKi1KWh5Y31ig9cPJo2bZp2P0T5OPvss+Ml93TfuBriyplqwirJDG2kNWPx8mZwyBqjSBFBiRSJq6hxkcLtK1qYeOrpthSyBouBBlENsfARCMO4m8UrVzL3I1kNk+Hn59lUyBKDM1zcaONUkLEIESAG6zIbD8daIxmtDF5lKdrzXEcWIawDqaJU4Om33cGcvfSwYmNlIeAP7r828a4QvZDIhgR2QdEmeA9rnOjfMoUKs69z15m29NsvuXlk2m8/srRq1SrF+lsCPDExQP9MFF+z1tlv3ZAxoqsSjTdSMC/6eIKnCAkCgoAgUFQREKUuDS1PAAkWeePiZCsbDBpZJ2AT63dQiKIpA3zEIn3gbB7cnz59eugSQT2YPWY9gpvmzZsXCmXuvuflN26lNuEyxzohO2qefT/WObOvGzZsCAvaQHpwYqNZFuBDDDhxGWMASn424f5DNEy3wmZmisEV6wIDUiLupZqwgFAW29pEmZNZx+i1zEHIkjsvFGvk2a1oEenOKEHuZ+L9RolHNm2MeIZALARFwMLsl7zKkuGLHNvvJteROSxWf/vb30yywI+pwDORdseFkii2uLS510uaSuMqigszrrEELaK90z2oLsy+zuAQ7ZhIvxSNl5fr7rp7ecZvGr+yhPVs6NChum9OdvIHeaOP53tiyxnvKd/SSpUqJbQ2GkUR63SikW39YijpBQFBQBBIFQKi1KUK2Th869evrzZt2qQmT56sB48oHwyOmNllc2oiMkJEomNTahNOm4ET7iwffPCBto6wHsytCKI4YMniHh8/ZtxtSxkBUdiXiVDQhH/no8gf54SBd69Ni1OV0G0G99SBIC4MyFFW8vLydOS8ChUqhNLZJyg4kSxppEH5bNKkierWrVuIJ+t3BgwYoK0HKMaGcAWDF/u6GWWCI4NO3H5MkAfKRTrb+kc+bdq00Vsn4F7kl8COtVbwtYm8sGDZVixckgh0g/JM24ARVkLWaTGjTbubMsKPc9KZc/ijcFA3uw52vtHO/cgSvO084WnKRZk5hwhMQxuBM4M2rrMGi8A4yCADOlz4/h977wEtRbH1fZdes2LAHDGDAVARFREF0QVLEEWCIFeCSFAQQRHwIlkJEuQYEBERlOSDL1wRQUEJCiJJclQkKSIPWUSQUN/61fvVvD19JnTP9IRzzt5rndMzPdW7dv1rV3WFXXv7IfJlZ44JBeEEIDAgrAGr/c5JlVc5/egS+TFI5BwrZSVv2uCgQYNUw4YNkz4/SPmi6Xwq8PRT77aeGOQyscOjZSQLARZFMI9l4kLsOlzXlylTxrT1rl27Jh3EG8wZxLsXiax89hpUX2d12+o1dWT1n/ZNfaELfslrv+SnD7EyxCu7Tef3Gks//egSZ+cwv7QOqQiNYa0jEllA4/2CZQV9rTWz5L2JKT27xbRN946y17LDh/Og6JyQICAICAJ5FgEtlDEENm/erBs1aqQLFSqkq1evrhcvXqz79++vBw4cqFetWmXkOnz4sB42bJguXrw4o2hdtWpVPXPmTL1hwwZdvnx5XatWLb1t27awMhw7dkxPmDDBPFOkSBE9ePBgDR83LVu2TNevX9/kTzryPXDggDuZp+87duwwcu7atUt36dJFw69o0aI6JycnF881a9bokiVLmvJQJuff7Nmzw/I7evSonjZtmq5YsaJJd+edd+oxY8ZELM/OnTt1r169TL4Wq7lz52rwgJCxUqVKofxGjhxp7jvlQS6+e6VoPCmHLZeTp7NuqHfwX7duncF++PDhpt6Ryz4Ln+7du5vvyL527dpQGbjvh7zqkjN/8qSM6EWzZs1Ccjnzhi91go5aPVq6dKlu2bKlnjJlit63b58fMU19oTfo0aOPPmr0s3DhwrpJkyaaNmPJr5xedQnMKeucOXNMu6QuaJ+LFi0K6ZKVwevVrSe2fp26YXkFjafXerf52+vo0aO1uz3a39DZzp0764MHD9pb5rp//37doUMHPXbs2LD7Xr8469RixNWpb05ezvaUaF/nrBvqnX7Y9k/IY2WKVFdOWaJ9TrRfitaH2Hy8lt2mj3V19oFO3Pns1AGvunTkyBE9YMAAPW/evLBsaYPcq1OnjuZd4ZfcbZg6b9OmTVi/4Jcn6SdPnmz4HDp0KJHH5RlBQBAQBLICgeOQIs/OSEVwQUAQEAQCRoCdJ3ZNieXI+TKh3AgQP5IdZ2JGus/QfbgW43sAACAASURBVPTRR8YRRl51uJS7tHLHLwJYEuCVEidHxYoVC3ucnbuOHTuaYPWpPJ8almmML+ySYuHBEYFEjgnEYC0/CQKCgCCQVgTE/DKtcEtmgoAgkO0IYGaHiReDPaHICGD6zOQXxxnWDJgrJrK4rY9mbh2Zm9zNbwhg4s8ZNzwLE8ietWP+cJiCaTUmve54k5nAgHbOcQfO3OJdV0gQEAQEgbyMgEzq8nLtpVB2YsDhlIJV+Hh/DO7yE+WVsoucwWtdjx49FMHqhw0bZjxAgnEkIl28dsHvzZs3DztTGYmX33vZUO/XXXedev/999WMGTNMCAPKiodRvJN27txZlShRwjhcym99SCbr3Y+eZFpOzly2atXKeEMlMDghL/irUaOGOd/NTh1OqjItJ6FYaKPEXESHhQQBQUAQyMsIiPllXq49kV0QEAQEAUFAEBAEBAFBQBAQBAo8ArJTV+BVQAAQBAQBQUAQEAQEAUFAEBAEBIG8jIBM6vJy7YnsgoAgIAgIAoKAICAICAKCgCBQ4BGQSV2BVwEBQBAQBAQBQUAQEAQEAUFAEBAE8jICMqnLy7UnsgsCgoAgIAgIAoKAICAICAKCQIFHQCZ1AaoA8Xfq1Kmjateurf74448AOQsrQUAQEAQEAUFAEMgUAvJ+zxTykq8gIAh4RUAmdV6R8pFu3Lhx6ueff/bxhCQVBAQBQUAQEAQEgWxHQN7v2V5DIp8gUHAROKHgFj34kp9//vnqvffeU6eeeqo65ZRTfGVw6NAhtXjxYhPb6ZxzzlHVqlXz9XwmE//5559q6dKlasGCBeree+9VpUqViimOLSvP/fDDD+r0009XL7zwQsxngvjR5jt37lxPcgaRZ0HksWnTJrVu3Tq1cOFCEwMKfU4FkQ96t2LFCtWmTRvT7lKRj/AUBAQB/wjY/jbd/bx/Sb09kcz73VsOkkoQEAQEgeQQkJ265PDL9fTWrVvVJZdcom688cZcv8W6cfjwYTVv3jz1xhtvKF6CeYn++usvNX36dJWTk6MOHjwYV3TKumTJEvXjjz+aP55PB1mMR4wY4UnOdMiUH/P4/fff1ZgxY9SsWbPUsWPHUlZE8vnvf/+rNm/enLI8MsmYNnLzzTerAQMGqKNHj2ZSlHyRt+CZ3mrMVD+fTCkxsWRh9u+//47IJtH3e0RmclMQEAQEgYARkEldgICuXbtWjR8/Xr344ou+dw3OOOMM9fzzz6saNWokLRETqw8++CDqiynpDFwMLrroItWiRQtVrFgx1y+Rv1LW5s2bq/bt26uaNWtGTpSCu+TbtGlTddddd6WAu7C0CIAvdZuqHTpnPo0bN7Zf8931iiuuMDuQDz74oPrXv/4VtXzpbu9RBcnyHwTP+BUUpC5lqp+PX8rwFJS5U6dO6v777zfvMCxmIlEy7/dI/OSeICAICAJBIyDmlwEiWrRoUdWxY8cAOSbGav/+/YpdDCFBQBDIuwgULlxYeZm0Snv3VseCZ3ycCqIunXzyyapt27aqVatW6t1331XsxkWibHm/R5JN7gkCgoAgAAKyU5fP9ABTxrffflv9+uuv+axk+aM4u3fvVkOHDs21i4pjnWnTpoUK+c8//6jffvst9F0+FDwEjhw5orZs2RLTHFvau3e9SDeeWmuFOV+6zMu9IxE5ZUHVpeOOO06dddZZijNz11xzTWRw5K4gIAgIAnkAAZnUZbCSdu3apV555RV15ZVXqhIlSqixY8cqBvNuYmDQu3dvYxrCCwgzxzfffDNsYsAZAEwaMXnp1q2bORdw2mmnKdJXrlxZ7dy5081W/fTTT6pBgwbqzDPPNPkPHjw4jGeuBzze4KzcY489ZvjC/5dffvH4ZO5kDIw4r/fAAw+YsmDax3ktDuG7iXv8RhrKjdMWnoVHNAIX8CE9f6NGjYqWNOJ9VrbBDexxwILjl4cfftiUndAWYGypR48eit0CHIhYsvV23XXXqe3bt5vbc+bMUawew5fzVJzRfPXVV41DEHBFXz799FPLwtfViy5RBmRFNwnN8frrr5s80TvOmzA4dpKzjtClp556Kulzbk6e1At5t2vXzkyII50vox5tWwKfeHLCM5IuffHFF6Gyo7eY61ImdOnzzz9P6oygV/1EB8mTnQF0KdLOgdUbL+2durT6zWcIHbP3brnlFuOgyVmnXj570SXKEikf531ksYRu0Q/SH/Ic7R5vg+hUpUqVErJACBpPK2us/pP6eeaZZxSms1OnTjXluOOOO8wZYnQTHX3yySfNpM/y83ONp0t++iU/uuRHRj9pveiS5cdCB/pAGwFH2j3HDdAZrkKCgCAgCBRYBLRQRhDYv3+/btGihR44cKA+cOCA+RszZowuXbq0HjlyZEim7du365o1a+ohQ4aYNPywa9cu/fTTT+uhQ4eG0jk/zJ49Wzdr1iyU3vmb/bxq1Spdr149vWDBAn306FG9d+9e3bdvX922bVt98OBBm8zzdceOHbpSpUq6e/fuevjw4ZryHT58WI8YMcLITzkiEWXlmWgEJpUrV9aLFi3Sx44d01u3btVNmjTRvXv3Nvztc8jcrVs38xtpSLtkyRJdtmxZvXjxYpvMYAI2YGRp9erV+qWXXjK42nter+Q7ffp0U/ZOnTqZ+gRL7vfs2VO3adNGHzp0KMQOPNx1A07t2rULq3cwQfYVK1boLVu2GP6k2bdvn548eXIuHqEMYnzwqksbN27U/fr107Vq1dLt27cP6cjy5cv1fffdpxcuXBiWy8yZM/UjjzyiV65caXRp/fr1Rj+rVaum0YtEaNasWfrZZ5819c3zYAomzZs3D9Nr6hE5O3bsGJJz7dq1+oEHHtDz5s0Ly9qLLtEuXn75ZV2lShXTFigLusS1atWqetSoUeZ7GGMPX7zqp5MV9VW7dm29Zs0a5+1cn+O1d+RHZ5o2bar/+uuv0PPUY58+fRJq7151icxou+XLl9e0M0vI9Nprr2l0x0nUEXLaNjx//nxdoUIFPWnSJD1u3DjTrzjT+/kcFJ7k6aX/pN5KlSoVwnjQoEG6XLlyGt3evXu36X+d/ZDXsnjRJb/9ks07ni7ZdH6vsfp5P7pE2rp16+qJEyea/p/3TJcuXXTr1q311KlTc/VNicjp7p/98pD0goAgIAhkCgHZqcvQdP6rr74yZlWNGjUyTlUIg/D444+rhx56KEyiffv2qQMHDqh77rkn5HwFBxTly5ePuIIf9nCUL5jZ4GWTMwS33367Ov74482qZ5MmTdSOHTvMjlCUR+Pe5tB5vXr1TJiCE044wew0sLOC5zm/hPnhJ598ovr06aNuu+02s3J/8cUXqy5duqjvvvsujOfs2bPVokWLVPfu3RVpWOVnVwyK5R2RnaiJEyeag/KJOPZgR40dH1aMr7/+eoMpK8jcr169ujGhdHozjeTwApzYcXETuxR4UUU3CPsAroUKFVJXX321qS93+njfvepSkSJFTJnQu9atW4d05KabbjJ6t2bNmlBWmJNi7vvss88aWdEl5MNZkN+wHiGmSpm6xIkOdQmBKTt1YIyOOQk5W7ZsGZKTXc+yZcuG7ZJ61aUbbrhBValSxbCnDJQFXeLaoUMHNXr06Ii73k55In1ORD/Bkr9kCflLly5t2OzduzfEDkwqVqxodDV00+MHr7oEO3Ycwc9pTkydYe5G27HEjtHMmTONnts2jNzsVu7Zs8c4VaIdJEpB4emn/6RPITwN/QE6TD+Obp599tkGl0TK4kWX/PZLicgR1DN+dIlQKegAeku/yWfeoVi98I6IF04nKJmFjyAgCAgC2YhA8iOGbCxVlsuEiSUv5rvvvjtscM7gyz3ox8YfkzAGmxBmNatXrw4bsPotLuftmGi4zw8w6GBAn0zgdEykeNlaYvCCWYzT5ND+Fu/KJO3CCy9UDNKdhDkig0EmdhCTxm+++cZMOPDEaemyyy5TU6ZMyTVRtr9j8sOEpH79+mayZO8ner3qqqvMBMA+T30mMyhHF+ABnXTSSQkNvq0sXP3qEvhx1sRSJP3ERBFdwvW+k5ItO5M3zPCcDn8YqDJ5dMpEnl7k9KpLtgxunty39RvJHNI+F+maqH5G4pXovXPPPVfh/XHVqlWGBeZ7GzZsUNdee21CLP3oEgNv+jraqDWdXb9+vTErpk7jEWbk2UR++k/av23DlIEFGncf76dsieiS1VubT7Jt0/IJ6upHlyLleeKJJ4a9cyKlkXuCgCAgCBQEBGRSl4Fa5sXMSrXdhYgnAivDeOViIsMZDSYiDIoSJXbjGDCfd955ZsDBS96+6DmP5z4z5Scf94AFvtxLhCeBpS+99NLQDqWVA54MVHAxzeo+k2RWat2TVNKxs+WcZFoerA4PHDjQTK6TwdLyyyvXoHWJXTMwTmYHJRJ2LA6wG12hQgVzHokdMuqMSUAiE2WvuhRJFnuPvIm95dx5tb/Fuiain7H4JfIbbYG+g9iB9D9MTKk39wTZD28/usQO1bJly8yuOeclFyxYkGshgAkPdc4ZOCbzpGNnht07LAqyhVLZf8YrYzboUjwZE/ndqy6x2ENaFgh4p9D/c9aVxQkWEIUEAUFAECjICMikLoO17+UlhHkgB+q3bdtmzARxxvHOO+8Y5x7JiM6gmcEJAyf3H2Z+eZG84GnL9eGHHxpTSeITvfXWWwk7LLD88sI1VboE7qyWB0lMxOvWrauIGYUjBMyV2e358ssvYzq+CVKGoHn50c+g84afdcbDTtPy5ctVyZIlE87Gry6xS8juJ46L6MtwbBNpUYtJvDVFZ/LOzixm4Qzms4ky3X9mWpeCrAs/usRCJGbtOMTCigMLFhynNGvWLKkd0CDLI7wEAUFAEMgUAjKpywDydveKl1E8mjRpkhmMMfm44IIL4iX39Dsr9BCmnKkmdgVYTY10Zixe3gzkOLPF6rSb8BSJqag1Z8JEa+PGje5k5jvldO8UcgaLQQFeDdnhGz58eMg0LCKTgG5G2jUMiHVcNqnQJXZhmSRQx6kgu3szYsQIs7s8ZMiQmGcko8ngVZeiPc99dBHClNEPgZFf/fTD32tazndxvg1vk8mYXpKfX12iHsuVK2d2xtllufXWW41JsVN2+gq8F+LZ8NtvvzWT96+//toEhabPzBZKZ//pLnO26JJbrmS++9ElzIc50/vxxx+bhQH6fDwD2/PTycghzwoCgoAgkNcRkEldBmoQBxIc6sYcyTnZYNDIzoSTMJNiQhRtMnDs2LGI7v2dPDg/M3ny5NAtnHqw0ss5Izd9//33Cbk3t3wwK3USJnOcu8I5gF/CBTiDT6eDBXiA07x588xheb5z3gzzLgaL5OckTHUmTJiQa8LG4AgCV3YCGDziyj/VxG4Fg1d2Ry0hczLnGC2feNcgdMmdBxNr9NntjGblypWhSZD7mXjfmcSjm06MeAanMbjfZ4fZL3nVJcsXPXa2Te6jc+wuXX755TaZp2si+umJcZRE7vbuTIaDCQbE1FkyppeJ6BImlOwQYl7pPoNpZcScF7NozG1xrkRbp4/LJLnxTGX/Ga+c2aRL8WT1+rtfXWK3t1+/fqa/T+WCklf5I6UjDAe70xxzcPdjkdLLPUFAEBAEgkBAJnVBoJgAj6pVq6pNmzap8ePHm8Ejkw8GMqzoE5waj4wQXuMISs3ZEohBDqYnn332mdkd4TyYeyLIxIGdLH5jQIRTFudOGQ5RiKHUv39/E0OJwSt/xFMixpz7bJrJ2MM/BoqUgfhNvMiYrOTk5Bgvd8WLF4/IgQlOpJ00EjN4qlWrlmrbtm2IJ2dtevbsaVb6mRhbwmwLXsR1AyOIKwNETHSsQwbkIp1z9498GjdurPr27WvislmeXq9gx1kr+DqJvNjBcu5iYT6Eoxsmz9QNGLFLyDktBtvUu5URfnwmnf0MfyYclM1ZBme+0T770SV4O/OEp5ULmfkM4ZiGOgJnBljc57wUjnHQQQZfmNv5IfJlZ47Bv/XWCAbz5883u7HOSZVXOf3oErKy4MI5VspK3rTBQYMGqYYNGyZ0ftCrfjpxIl8mOe6FEmcaPsdr7870nEXF0VCyZ9T86JLNn0Eu+eLRMpLVAQstmNwycWE3sX379qpMmTKm/+jatWvSQbyDwtNr/2nbi20r6LVtU/QZ9Hvol1/yqkt++iUrgx9dss94uVLuaP28H13i7Bzml9bJ1RNPPBGyuEh2UY7JOwuQ7AZ6saKJV27e6ZwHpQ0LCQKCgCCQFgQyFUtB8tV68+bNulGjRrpQoUK6evXqJp5a//79Tawz4iBBxDAbNmyYLl68OKNoEyuL2E4bNmwwsZ+I0bVt27YwOIkBNWHCBPNMkSJF9ODBg8NiutnEy5Yt0/Xr1zf5k87GzLO/+7kSjww5iaFH3CD4FS1aVOfk5ITFFYMn8ZtKlixpykOZnH/uuE3E0Js2bZquWLGiSXfnnXdqYlmBi5t27type/XqZfK1WM2dOzcUV8zG0rP52XiATnmQK15cMGe+0XhSDpuPk6ezbqh38F+3bp3Bnvh+1Dty2WfhQ3w2vhMHkBhsXPkeK76fU0b72asuOfMnL8pILEXiN1m5nHnDlzpBR60eLV26VLds2VJPmTLFxNazMni5ghF6gx49+uijRj8LFy5sYhDSZiz5ldOrLtlYXXPmzDHtkjLTPm2sRJu/32s8/bT8nOWyeMeqb6dOxWrv8D9y5IiJeblnzx6bXUJXr7rkZj569OiwGJHO32kHnTt3zhU3j1hkHTp00GPHjnUm9/w5VXjG6j+d/QLthliZts9DHiuTs2/wXCCtdTxdcuaP7pAfFK1fsnn70SX7TLSrs1916jGfkcOSV11CdwcMGJAr/iTtmpiUderUSSjWqLtvs7ImWje2XMSGdMcptb/JVRAQBASBVCBwHEzTMnuUTAQBQUAQyAMIsEvErimxHDkLlp+InW7M17LRGRIxKdnFJg6l+wzdRx99ZBxhZKPc+Uk/srksWCc899xzxnFSsWLFwkRl565jx46qV69evs+8hjEK6Au7pFiNcOwgkaMHAYkhbAQBQaCAISDmlwWswqW4goAgEBsBTOIwp2Vglh8IM7CRI0eac4+YsHK+MBsJc2om1DjOsKbFXJEZt/XRTLizsSwiU/AIYPaKx1a8FXPOmvVo/vCkirk2Jr3JnBMNSmL6Do5Q0M7w2CskCAgCgkC6EJBJXbqQzmP5EAMOpxSsmMf7YyCWnyivlF3kDF7revToYYLVDxs2zHhrBONIRLp47YLfmzdvHnamMhKvVN8jhiPnNjnHy7nXSAHHs0GXCLnw/vvvqxkzZpgQBuCH11I8nnbu3FmVKFHCOHHKb/1SXtGlTMvJmctWrVoZb6gNGjQw8SoJe1GjRg1zZpydOhxfZVpOwrvQ7om5iA4LCQKCgCCQLgTE/DJdSEs+goAgIAgIAoKAICAICAKCgCAgCKQAAdmpSwGowlIQEAQEAUFAEBAEBAFBQBAQBASBdCEgk7p0IS35CAKCgCAgCAgCgoAgIAgIAoKAIJACBGRSlwJQhaUgIAgIAoKAICAICAKCgCAgCAgC6UJAJnXpQlryEQQEAUFAEBAEBAFBQBAQBAQBQSAFCMikLkBQiZVTp04dVbt2bfXHH38EyFlYCQKCgCAgCAgCgkBeQGDcuHHqiiuuUGPHjjVhF/KCzCKjICAI5H0EZFKXgjqkQ//5559TwFlYCgKCgCAgCAgCgkC2I0B8yJkzZ6qDBw9mu6ginyAgCOQTBE7IJ+XIimKcf/756r333lOnnnqqiQflR6hDhw6pxYsXmzhM55xzjqpWrZqfxzOa9s8//1RLly5VCxYsUPfee68qVapUTHlsWXnuhx9+UKeffrp64YUXYj4TxI8237lz53qSM4g8CyKPTZs2qXXr1qmFCxeaeE3ocyqIfNC7FStWqDZt2ph2l4p8hKcgIAikFgHa8oYNG9TWrVvVd999Z94HxC3Mq1SrVi0TO2/atGmK+HpCgoAgIAikAwHZqQsYZV5Kl1xyibrxxht9cT58+LCaN2+eeuONNxSTnbxErEhOnz5d5eTkeFqVpKxLlixRP/74o/nj+XSQxXjEiBGe5EyHTPkxj99//12NGTNGzZo1Sx07dixlRSSf//73v2rz5s0py6MgM8acnEWqv//+u0DB8M8//ygCWd9zzz1icZGmmqct8/5jgWb58uUp7TeCKBL92hdffGEWMiPxO3LkiCkHgdFPOumkSEnkniAgCAgCgSMgO3UBQrp27Vo1fvx49eKLL/reNTjjjDPU888/r/bt25e0RJh7jBo1Sj3xxBO+5Ugk84suuki1aNFCff/9954ep6zNmzc3aZHzl19+8fRcsonIt2nTpmr16tXJspLnYyBw1113KXbnunbtGiNV8j+Rz9GjR9XHH3+cPDPhYBCg73jttdfUnDlzzC4oOw7169cvUOiceOKJqnLlyuqGG25Ql156acyyY2mgtVZlypSJmS4//hhk2WnL/O3cudPs2GUrXosWLVL/+c9/FAseWNbMnj07l6gsHg4ZMkTdfvvt6u677871u9wQBAQBQSBVCMikLkBkixYtalZ4A2SZEKv9+/crVj6FBAFBQBDwg8DJJ5+s2rZtq1q1aqXeffddYw7n5/n8kPa4445T5cqVM3/xyrN+/Xp15ZVXxkuWL38viGUvXry4Gj16tNq4caNq3LhxxHplUYBFTiFBQBAQBNKNgJhfphvxFOeHKePbb7+tfv311xTnJOwTQWD37t1q6NChuUzacKzD+QtLmID99ttv9qtcBYEwBNgdYrcgaNNlJjRnnXWW4nzwNddcE5ZnQfqyd+9eM3CPVmbw5wzxW2+9FS2Jr/t5qb0HXXZfQGU4MaaU5557rpnIY6EiJAgIAoJANiEgk7oM1sauXbvUK6+8Yl4QJUqUMO6Pebm7icFb7969VbFixRSDLq5vvvlm2MSAcy+YNGJi2K1bN3MW5rTTTjPpMSXCrMVNP/30k2rQoIE688wzFfkPHjw4jKc7vdfvnJV77LHHDF/4J2NeyQCC83oPPPCAKQsmOpzXwumJm7jHb6QBJ5y28Cw8ohG4gA/p+cMc1A+xKwpuYI8DFkySHn74YVN2QluAsaUePXqowoULGwci9p6tN5wCbN++3dzG9I0dE/gOGDDAnNF89dVXzXkTcGVn4NNPP7UsfF296BJlQFZ0k9Acr7/+uskTveOMFedFnOSsI3TpqaeeSvqcm5On1fl27dqZCTEml26iHm1bAp94csIzki5xTsaWHb3FXJcyoUuff/55Qmd9/OIZr47QmWeeeUY9+OCDaurUqaZt3HHHHeZ8KuWm/E8++aSZ9DlxSlV7d+YR6TPnj2bMmKGqV69usARP2gbnai252yF1ADnv08ac5/u2bNlidA1+lJn6/+CDD0xfxtUv2bxwRX/11VdHNeulfR5//PEKzDkHxtm7WP0H/dKwYcOMXMhKn8i5MUupbO/kwa4SesxkhD/OC/LuseRHP/2W3eYR1NWLLtm86KcIKcC7jfrhHYJnavqnSpUqiTWLBUqugoAgkG8QkEldhqqSFfbOnTubFXHOeDE4gL766qswiRjgPfvss+ZljA0/g10mDwwKMAOxhMdNJgH8jp1/s2bN1IEDB8z3L7/80jxv03IlTyZ/zz33nNqzZ495hgkKMkWaMDmfjfWZSRSycc6JgUPFihVV+/btcw0wY/Fw/vbJJ5+ovn37mokFL/QJEyaYAeLAgQPDJhfI3KdPH/MbaUjLKjrlwUNiNGKQA6+XXnrJyFuvXr1oSSPex9QGs1sGTlOmTDH1yMSQerv11luNCZudqHfq1EnhpMVJ1Bs7q0xYLJUtW1Z1797dlIXBx/z58039gCnPYxb39ddfhw1w7bOxrl516eKLL1YsCODBEsc9999/v5mYM5Fk0uzG89tvvzWLDCw0oEsMrv/nf/7H6F8seWL9hge8//N//o/RI3QaDAoVKqQ402LxtM+jZ+j+o48+auRkooOsLC44yYsuMZhn4sCEA5w7dOig2LUZPny4OSeTSNwpP3h6qSN0Bm+fJ5xwglk0YCLaqFEj1bp1a3MODNnBjPqzlKr2bvnHuqILLE6wOACW6MjTTz9t9MSaidMOP/vsM/Xyyy+rLl26mN/gadsn7aN///6hM8LgRL/CRJH6X7lypcEDRxuku+WWW2KJFPE38qKvxPEOeh+NaJ/gS/9KP0t/y3f+3P0HEwscSCEj6Sg7fS59Ad5hoVS1d3gvW7bMyMRCEws0WHCwYERfaduRH/30U/Zo+CVz34suWf70ASwm8E7lfdCrVy/TpnFe0qRJE7PAYNPKVRAQBASB/ICATOoyVIu8aPByyWCMQRp/jz/+uHrooYfCJMJxCoMHVoNJA+GEonz58gmfd2FCyaCFczMc5mbVmRVkXnQ7duwwO0JhQvj4gqMFBjaEKWDQyWCCnRXnqrxXdpgfMhBnAHLbbbeZ1VYGIAz6GPQ7eTJgYsDPZIg0rMyyKwbF8o7IQGfixIlmkJWI630GSOz4sFNw/fXXG0zBkvsMOCmD05tpJPfW4MQOq5tYWcaLKvUOnuDKxIaJB3n4Ja+6VKRIEVMm9I6JgtWRm266yejdmjVrQlljTsqklIUHZEWXkA9nQaecckoond8P1GXNmjVNXfIs5WVgD8buuE/I2bJly5Cc7Hoy+HTuknrVJZxjVKlSxYhLGSgLusSVCR4LKezo+CE/eHqtI/JHXwl9gq6BD30E5T777LPNQoOVMZXt3eYR60r7I8yJxRId4Tu67HQMRTlYBGIHjjq1tG3bNqMLtAFLTFj5TnraD5/pS5k80VfEC6ti+US7Rmqn0dLGuo8HWCauTOSoI8pOe2IyiC7ZiRU8gm7vtt7Z1a1atarBib6EfpFdaLvr6Uc/Y5U1Hb951SXKRow4+kz7PihdurR5HzGxpm9x6lM6ZJc8BAFBQBBINQLiKCXVKuSfhwAAIABJREFUCEfgz4ucSQiesZyDcwaP7sEE51pYibfEbhqDHgas7rQ2Tbwrq7VMNNxnZpCFFzznuxIdFLGzxCDLEgNMzF9YlcZczA8xsL/wwguVO14RISOYSDGxY4DEpPGbb74xEw7nOYfLLrvM7J7ZybA7b1b7mZBwqJ0BZrJ01VVXmQmA5UN9MohLlKhfeECc5WDQmwz51SXw43yVpUj6yeAQXbr55pttMnNNtuxM3tgVw+STQRlE+Zk8sjvqJC9yetUly9fNk/u2fglbct5559mknq9unpHw9FNH6JbVD4RAzyP1Cals714K79yFZueKSTFWB87FDsuHhQEmZjjhoN+gbeNVGHPNeIReOPueeOlT/Ts7d1hV0Ee52y66hLk1Ey/r8j7o9g6GTChx+uLUE0wxGzZsmGshyYt+phqzePz96FIkXlggCAkCgoAgkF8RSHzEmV8RSUO5GKiwEm0Hq/Gy5MWPKRgTGSZGTER4YSdK7MYxYGZgysve/jFIxCTTfWbKTz7uQSW8uZcIT8z8cCnunpTBk0ERgz1WZJkkMxB0T1JJx2Qt0kCPHQLMLplcJ4OlH2yyIW3QusSuGRgHverN4gC70RUqVDA7GOxqUGcMjhOZKHvVpVh1RN64K480GYn1nN/fgq6jVLZ3L2VjcgP+nGVi8s+kApNd526c5cOiDAtKmBfzHDszTNbcu+hM+sGJxRz6FvoBzjxee+21ZqfS8svklbbBhPrf//53qI+1fS07wtQLZoGpIvSUtsnCmpOYREayDHCmydbPXnWJdwb9B6bwTGx5jt1ddu+YZAsJAoKAIJAfEZBJXQZr1f2yjSQK5oE4PcAECTNBVn7feecd49wjUnqv9xg0M6jgZef+c58L8coz0+m84Gll/PDDD42pJGdbOHvHrl1+p1TpEri7d8+SxZKJeN26dc2ODmf0MFdmZ5szT+hrfqVU1VGm2jt1xTlMzA+Jd8eZN87OYdrKLrybmPQwkae+6fOY/JUsWdKdzCxIYd4Mb/gwScKCAbNG98JSrofTfGPkyJG5+lhwiXTWOWjRUtE2g5bRKz+/uoQeMbnjWAMLQezyc8SABQEhQUAQEATyIwIyqctArTJwYeDBICQeTZo0yZgfMvm44IIL4iX39Ls1NcSUM9XEriSr6ImsDPPy5cyW89yJlRfTJUxFrckZZjU4K4lElNO9U8gZLAaDeDVkhw9HGMiaaoq0a5jqPC3/VOgSesxuBHWcCrIr7jiIYXeZoL6xzkhGk8GrLkV7nvvoIoRDjVRRKuoone3djQsTMzwO4iyEnRMv+o/jIRYJ2EWnriMNwletWqU424nzIMw5aft4iLXnaN1yZOI7O2LIY73aplsGzrSyS8WOppvoD9PR/7vzTea7H12iL8cDKrvDLAwwIWT3F6dPvH+FBAFBQBDIjwjIpC4DtcrLlsP8eCZzTjYYNHLWxEmc32FCFG0whPlOPG+V/D558uQQW5x6sILLOSM3ff/998as0X3f63e3SRUmc5y7woGDX8Jl+IYNG3LFawMnvIXiJAFi8ISTCEyxnI4X+I0BDd4w3RM2u5oPrqze8sLHtXeqiXMryMIgwxIyc44x1RSELrllZGKNPrsnWngjtJMg9zPxvjOJRzedGPEM563wasgOs1/yqkuWL3rsbJvcR+cwmb788sttssCvqaijVLb3eACg22AZ6ywT/ZPTDJFJM6bmLLzQXuyk1J0Xnnb79etn2n0qFxbc+cb6zk4k/R1EH0O/R5/qbgv0S0x23f1SLN5+f8MxDYsi7ncKfNBlpyMhv7wjpXeWPdLvyd7zq0uYv2Jij+k2TrWoA6eeJStPEM9jIVKnTh1zZpRdeiFBQBAQBJJBQCZ1yaCXxLN4I9u0aZMaP368GTzykuflw+CH4NSYWkK8mAlKbd2T82LC5AgTJnZHOA/mfmkzEGJ1mN94ibHi7dwpwyEKHtFw/Y37dwav/PEZN/Dus2lei8ngnjIwWGBAzkuYFXoCGRcvXjwiGwY1kXbSSMxgtFatWqpt27Yhnqw89+zZ04QLYGJsCVMbeOE63Q6guPJSxzTLOipALtI5d//Ip3HjxiZ0QiIvVrDjrJV7gEZe1JFzF4tzjLiXZ6BH3YARu4Scf2HXgXq3MsKPz6Sznykvg2TK5iyDxSHW1Y8uwduZJ3ytXMjMZ4gzUNQRODOw5j5BmXGMgw4y6GaF3Q+RLztznIfBBT4EBoQ1YEfGOanyKqcfXSI/Flw4x0pZyZs2OGjQIONgIpHzg17l9FpHti5sPYCZrS/0kTaF7FAi7Z2JFpMTdsS8WBSYjCL84ywc/QKmhvBENkKe0E7hyz1MLZ27SdYEk510JuORiLNzmF9aZ0dPPPFEaOc9iMUZsIzWL1l5mDCxq8hOEOWgTfLZaVbK7iQWFvRZ9F0QV7wPc76QiZ+tS1t/QbV3sMfkFY/AmO2DPX98ZkLMIoklr/pp03spu03r50rZmYy5Fwfh4UeXwBXzbRb8iK1H+IsyZcqYd1HXrl3D9M2PfDYtiy/0a/Rz1H2yxAQ/HQt7ycopzwsCgkCWI6CFMobA5s2bdaNGjXShQoV09erV9eLFi3X//v31wIED9apVq4xchw8f1sOGDdPFixdnFK2rVq2qZ86cqTds2KDLly+va9Wqpbdt2xZWhmPHjukJEyaYZ4oUKaIHDx6s4eOmZcuW6fr165v8SUe+Bw4ccCfz9H3Hjh1Gzl27dukuXbpo+BUtWlTn5OTk4rlmzRpdsmRJUx7K5PybPXt2WH5Hjx7V06ZN0xUrVjTp7rzzTj1mzJiI5dm5c6fu1auXyddiNXfuXA0eEDJWqlQplN/IkSPNfac8yMV3rxSNJ+Ww5XLydNYN9Q7+69atM9gPHz7c1Dty2Wfh0717d/Md2deuXRsqA/f9kFddcuZPnpQRvWjWrFlILmfe8KVO0FGrR0uXLtUtW7bUU6ZM0fv27fMjpqkv9AY9evTRR41+Fi5cWDdp0kTTZiz5ldOrLoE5ZZ0zZ45pl9QF7XPRokUhXbIyeLn6kdNLHa1evTqkA8hJv2HbE3nZ/Jx6h5xe2ru7nq0eunl5KbdNs379+rB+5rXXXjM69dZbb+mLL77Y6I5to/aZ//3f/9U9evTI1Xfw+5EjR/SAAQP0vHnzbHJzpX65V6dOHU0/5JfcbdlL2bdv32700vbhtGU3gem7774b6sNtX2/T2foivyDbO/zBlfeFbUe0zz59+oS1SWf+Xtq7ldtL2W3aeFfbx1nM7RX9dr6TvOoS9dC5c2d98ODBsKz379+vO3TooMeOHRt23+sXZ79uZeTq7A+98rLp9uzZoxs2bKgXLlxob8lVEBAEBIGEEDiOp7J83iniCQKCgCCQNgRY2WfXlN0UdiSEsgsBdsRwvIIDHUJeOImdu44dO5pA06k8++jMUz5nHwLEN8Uigpim7jN0H330kdkhzRaHYMiJMx30Wfqb7NMlkUgQyEsIiPllXqotkVUQEARSjgBmi5jTYqomlH0IYE6KR0y81hJUnnVJ/nCYgtku5o7O+IrZVwKRKNUIYJrP4gyOhzAthbhiwk3oi2jHAVItl5s/xyowuccbrEzo3OjId0FAEPCLgEzq/CJWQNITAw6nFKxyxvvj5ZmfKK+UXeQMXut69OihHnroITVs2DATtBmMIxHp4rULfm/evHnYmcpIvPLavUyXnfNSrVq1Mp4MGzRoYNzV47K+Ro0a5uwwO3U4QMq0nEHXq7R374hed9116v3331czZsww3lNpi3hRxXtu586dTWD7bMATmTjvl01eW72jLCkFAUEg2xAQ88tsqxGRRxAQBAQBQUAQEAQEAUFAEBAEBAEfCMhOnQ+wJKkgIAgIAoKAICAICAKCgCAgCAgC2YaATOqyrUZEHkFAEBAEBAFBQBAQBAQBQUAQEAR8ICCTOh9gSVJBQBAQBAQBQUAQEAQEAUFAEBAEsg0BmdRlW42IPIKAICAICAKCgCAgCAgCgoAgIAj4QEAmdT7AipeUGEl16tRRtWvXVn/88Ue85PK7ICAICAKCgCAgCOQBBOT9ngcqSUQUBAo4AjKpS4ECjBs3Tv38888p4CwsBQFBQBAQBAQBQSBTCMj7PVPIS76CgCAQD4ET4iWQ370jcP7556v33nvPBBE95ZRTvD+olDp06JBavHixInbOOeeco6pVq+br+Uwm/vPPP9XSpUvVggUL1L333qtKlSoVUxxbVp774Ycf1Omnn65eeOGFmM8E8aPNd+7cuZ7kDCLPgshj06ZNiqC6CxcuNHHa0OdUEPmgdytWrFBt2rSR4L2pAFl4CgIJImD723T38wmKG/exZN7vcZlLAkFAEBAEAkBAduoCANHJYuvWreqSSy5RN954o/N23M+HDx9W8+bNU2+88YbiJZiX6K+//lLTp09XOTk56uDBg3FFp6xLlixRP/74o/nj+XSQxXjEiBGe5EyHTPkxj99//12NGTNGzZo1Sx07dixlRSSf//73v2rz5s0py0MY5x8E6HNuvvlmNWDAAHX06NH8U7AsLUmm+vlk4MDEkoXZv//+OyKbRN/vEZnJTUFAEBAEAkZAJnUBAsou2/jx49WLL77oe9fgjDPOUM8//7yqUaNG0hIxsfrggw+ivpiSzsDF4KKLLlItWrRQxYoVc/0S+Stlbd68uWrfvr2qWbNm5EQpuEu+TZs2VXfddVcKuAtLiwD4Urep2qFz5tO4cWP7NU9c09028wQoaRLyiiuuMDu6Dz74oPrXv/4VNdeCXEdBlj1T/XzUio3yA2Xu1KmTuv/++807DIuZSJTM+z0SP7knCAgCgkDQCIj5ZYCIFi1aVHXs2DFAjomx2r9/v2IXQ0gQEASyCwFpm5mrj8KFCysviwAFuY4KYtlPPvlk1bZtW9WqVSv17rvvKnbjIlG2vN8jySb3BAFBQBAAAdmpy2d6gCnj22+/rX799dd8VrL8UZzdu3eroUOH5tpFxbHOtGnTQoX8559/1G+//Rb6Lh/yPgLSNjNbh0eOHFFbtmyJad4eZB1prRXmfOkyL08W3SDLnqws6Xz+uOOOU2eddZbizNw111yTzqwlL0FAEBAEAkVAJnWBwumP2a5du9Qrr7yirrzySlWiRAk1duxYxWDeTQwMevfubUxDeAFh5vjmm2+GTQw4A4BJIyYv3bp1M+cCTjvtNEX6ypUrq507d7rZqp9++kk1aNBAnXnmmSb/wYMHh/HM9YDHG5yVe+yxxwxf+P/yyy8en8ydjIER5/UeeOABUxZM+zivxSF8N3GP30hDuXHawrPwiEbgAj6k52/UqFHRkka8z8o2uIE9Dlhw/PLwww+bshPaAowt9ejRQ7FbgAMRS7berrvuOrV9+3Zze86cOYrVY/hy/oczmq+++qpxCAKu6Munn35qWfi6etElyoCs6CahOV5//XWTJ3rHeRMGx05y1hG69NRTTyV9zs3Jk3oh73bt2pkJcaTzUNSjbUvgE09OeEbSpS+++CJUdvQWc13KhC59/vnnCZ0RtHXspW2Cu9VFPkPog713yy23GGdKicgZdHv3oku0J7fslMl5n/JZQrfoB+kPeY52j7dBdKpSpUoJWSCQF3XITgttM9JOjJ86srLGwhN+zzzzjMLUc+rUqaYcd9xxhzlDjG6io08++aSZ9Fl+fq7x+jo//VIiZfcjq5e0XnTJ8mFijj5Qp+BIu+e4ATrDVUgQEAQEgQKLgBbKCAL79+/XLVq00AMHDtQHDhwwf2PGjNGlS5fWI0eODMm0fft2XbNmTT1kyBCThh927dqln376aT106NBQOueH2bNn62bNmoXSO3+zn1etWqXr1aunFyxYoI8ePar37t2r+/btq9u2basPHjxok3m+7tixQ1eqVEl3795dDx8+XFO+w4cP6xEjRhj5KUckoqw8E43ApHLlynrRokX62LFjeuvWrbpJkya6d+/ehr99Dpm7detmfiMNaZcsWaLLli2rFy9ebJMZTMAGjCytXr1av/TSSwZXe8/rlXynT59uyt6pUydTn2DJ/Z49e+o2bdroQ4cOhdiBh7tuwKldu3Zh9Q4myL5ixQq9ZcsWw580+/bt05MnT87FI5RBjA9edWnjxo26X79+ulatWrp9+/YhHVm+fLm+77779MKFC8NymTlzpn7kkUf0ypUrjS6tX7/e6Ge1atU0epEIzZo1Sz/77LOmvnkeTMGkefPmYXpNPSJnx44dQ3KuXbtWP/DAA3revHlhWXvRJdrFyy+/rKtUqWLaAmVBl7hWrVpVjxo1ynwPY+zjS7y2SV7Ub9OmTfVff/0V4gzmffr0CbVNv3IG3d696hIFoO2WL19e084sUc7XXntNoztOoo4ou23D8+fP1xUqVNCTJk3S48aNM/2KM72fz8hcu3ZtvWbNmpiPxasjHvaCJ/mUKlUqVG+DBg3S5cqV0+j27t27Tf/r7IdiCuX40Utf57dfsuy9lN2m9XON1c/70SXS1q1bV0+cONH0/7xnunTpolu3bq2nTp2aq2/yIyNpkdPdP/vlIekFAUFAEMgUArJTl6Hp/FdffWXMgBo1amScqpx66qnq8ccfVw899FCYRPv27VMHDhxQ99xzT8j5Cg4oypcvH3HFOezhKF8ws8HLJmcIbr/9dnX88cebVc8mTZqoHTt2mB2hKI/Gvc2h83r16pkwBSeccIJZGWdnBc9zfgnzw08++UT16dNH3XbbbWbl/uKLL1ZdunRR3333XRjP2bNnq0WLFqnu3bsr0rDKz64YFMs7IjtREydONAflE3HswY4aOz6sGF9//fUGU1aQuV+9enVjQun0ZhrJQQM4sYvjJnYp8KKKbhD2AVwLFSqkrr76alNf7vTxvnvVpSJFipgyoXetW7cO6chNN91k9G7NmjWhrDAnxdz32WefNbKiS8iHsyC/YT1CTJUydYkTHeoSAlN26sAYHXMScrZs2TIkJ7ueZcuWDdsl9apLN9xwg6pSpYphTxkoC7rEtUOHDmr06NERd72d8iTzmbxKly5tWOzduzfECvkrVqxo9IqbfuRMRXv3qkvIyg4Z+DnNiakzzN1oO5bYMZo5c6bRc9uGwYLdtT179hinSrSDRAnd5C9Z8oMnfQrhaegP0GH6cXTz7LPPNrgkIouXvs5vv5SIHEE940eXCJWCDtAW6Df5zDsUqxfeEfHC6QQls/ARBAQBQSAbEUj+DZeNpcpymTCx5MV89913hw3OGdC5B/3Y+GNqxSAOwqxm9erVYQNWv8XlvB0TDff5AQYdDOiTCZyOiRQvW0sMXjCLcZoc2t/iXZmkXXjhhYpBupMwR2QwyMQOYtL4zTffmAkHnjgtXXbZZWrKlCm5Jsr2d0x+mJDUr1/fTJbs/USvV111lZkA2Oepz2QGkegCPKCTTjopNKC3/P1e/eoS+HHWxFIk/cREEV3CVbyTki07kzfM8JwOfxioMnl0ykSeXuT0qku2DG6e3Lf1G8l8zz4XxPXcc89VeGpctWqVYYep3YYNG9S1116bi70XOVPR3v3oEgNv+jraqDWdXb9+vTErpk7jEWbk2UR+8KT92zZMGVigcffxfsqWSF9n9dbmk2zbtHyCuvrRpUh5nnjiiWHvnEhp5J4gIAgIAgUBAZnUZaCWeTGzUm13IeKJwMowXrmYyHBGg4kIg6JEid04BsznnXeeGXDwkrcves7juc9M+cnHPWCBL/cS4Ulg6UsvvTS0Q2nlgCcDFVxMs7rPJJmVWvcklXTsbDknmZYHq8MDBw40k+tksLT88so1aF1i1wyMk9lBiYQdiwPsRleoUMGcR2KHjDpjEpDIRNmrLkWSxd4jb2JvOXde7W9BXtFb2jlx/ugrmESCsXsyGy1Pt5ypau9+dIkdqmXLlpldc85LLliwINdCABMe6pwzcEzmScfODLt3WBRkC6UKTy/lS6Sv88I302m86hKLPaRlgYB3Cv0/Z11Z8GABUUgQEAQEgYKMgEzqMlj7Xl5CmAdyoH7btm3GTBBnHO+8845x7pGM6AyaGZwwcHL/YeaXF8kLnrZcH374oTGVJD7RW2+9lbDDAssvL1xTpUvgzmp5kMREvG7duoqYUThCwFyZ3Z4vv/wypuObIGXIJC/rOIddoeXLl6uSJUsmJU7Q7d2vLrHzyK4ijovoy3BsE2lRi0m8NUVn8s7OLGbhDOaziYLG02/Z/PR1fnmnO70fXWIhErN2HGJhxYEFC45TmjVrltQOaLrLLPkJAoKAIJAKBGRSlwpU4/C0u1e8jOLRpEmTjPkhk48LLrggXnJPv7PqD2HKmWpip4HV1EhnxuLlzUCOM1uRPILiKRJTUWvOhInWxo0bI7KknO6dQs5gMSjAqyE7fMOHDw+ZhkVkEtDNSLuGAbGOyyYVusQuLBMP6jgVZHdvRowYYXaXhwwZEvOMZDQZvOpStOe5jy5CmEemmjiLxVk0PENGM72MJoNbzlS0d7+6RD2WK1fO7Iyzy3Lrrbcak2JnGegr8F6IZ8Nvv/3WTN6//vprExSaPjNbKBV4ei0b7c1vX+eVd6bS+dElTJI50/vxxx+bhQH6fDwD2/PTmSqD5CsICAKCQDYgIJO6DNQCDiQ41I05knOywWCMnQknYXrFhCjaZODYsWMR3fs7eXAmZ/LkyaFbOPVgpZdzRm76/vvvjVmj+77X75iVOgmTOc5d4RzAL+ECnAGt08ECPMBp3rx55rA83zlvhnkXg0XycxKmOhMmTMg1YbNmouDKTgCDR1z5p5rYrWDwyu6oJWRO5hyj5RPvGoQuufNgYo0+u53RrFy5MjQJcj8T7zuTeHTTiRHP4DQGl/7sMPslr7pk+aLHzrbJfXSO3aXLL7/cJkv66m6bToY4g2DwCr7RTC+9yJmK9p6ILmFCya4j5pXuM5i23JjzYhaNuS3OlWjr9HGZJHcdpQJPr+VLpK/zyjtSOnfZI6VJ9p5fXWK3t1+/fqa/T+WCUjLlIgwHu9Mcc3D3Y8nwlWcFAUFAEIiFgEzqYqGTwt+qVq2qNm3apMaPH28Gj0w+GMiwCktwajwyQniNIyg1Z0sgBjmYnnz22Wdmd4TzYO6JIBMHdrL4jQERTlmcO2U4RCGGUv/+/U0MJQav/BFPiRhz7rNpJmMP/xh8UgbiN/EiY7KSk5NjvNwVL148IgcmOJF20kjM4KlWrVqqbdu2IZ6ctenZs6dZ6WdibAmzLXgR1w2MIK4MEDHRsQ4ZkIt0zt0/8mncuLHq27evictmeXq9gh1nreDrJPJiB8u5i4X5EI5umDxTN2DELiHntBjAU+9WRvjxmXT2M/wZyFM2Zxmc+Ub77EeX4O3ME55WLmTmM4RjGuoInBlgcZ/zUjjGQQcZfGFu54fIl505Bv/WAyQYzJ8/3+zGOidVXuX0o0vIyoIL51gpK3nTBgcNGqQaNmyY1PnBeG3TiRPnRnEKFOs8mRc5U9He/eiSLRODXMqCR8tIVgcstGByy8SFHcr27durMmXKmP6ja9euSQfxph6ZNLoXnqx89hqvjrziaduLbSvotW1T9Bn0e+iXX/La1/npl6wM8cpu0/m9Uu5o/bwfXeLsHOaX1snVE088EbK4SHZRjgksC5DsBnqxoomHAe90zoOic0KCgCAgCKQFgUzFUpB8td68ebNu1KiRLlSokK5evbqJp9a/f38T64w4SBAxzIYNG6aLFy/OKNrEyiK204YNG0zsJ2J0bdu2LQxOYkBNmDDBPFOkSBE9ePDgsJhuNvGyZct0/fr1Tf6kszHz7O9+rsQjQ05i6BE3CH5FixbVOTk5YXHF4En8ppIlS5ryUCbnnztuEzH0pk2bpitWrGjS3XnnnZpYVuDipp07d+pevXqZfC1Wc+fODcUVs7H0bH42HqBTHuSKF8fKmW80npTD5uPk6awb6h38161bZ7Anvh/1jlz2WfgQn43vxAEkBhtXvseK7+eU0X72qkvO/MmLMhJLkfhNVi5n3vClTtBRq0dLly7VLVu21FOmTDGx9awMXq5ghN6gR48++qjRz8KFC5sYhLQZS37l9KpLNlbXnDlzTLukzLRPGyvR5p/I1Vn/sdomvI8cOWLiU+7ZsydiVn7lDLK9e9Ult+CjR48OixHp/J120Llz51AsPvsbscg6dOigx44da2/5ujr1xOpvrPbjtY5i4ensF2g3xMq0fR7yWJmcfYOfQiXa10Xrl2zeXstu08e6OvtVJ+58Rg5LXnWJ9jBgwIBc8Sdp18SkrFOnTkKxRt19m5U10bqx5SLepDtOqf1NroKAICAIpAKB42CaltmjZCIICAKCQB5AgF0idk2J5chZsEwRu9KYmkVzXJQtcgaFDzEp2cUmDqX7DN1HH31kHGFEwyIoGYRP9iKAdcJzzz1nHCcVK1YsTFB27jp27Kh69eqVljOvYZlH+MIuKVYjHDtI5OhBBJZySxAQBASBuAiI+WVciCSBICAIFCQEMInDnJaBWboJk62RI0eaM4qYm3IWMBplUs5oMiVzH3NqJqo4zrCmxVzBAbf10Uy4k8lTns07CGD2ihdYvBVzzpr1aP7wpIq5Nia90c6eprOU9B0coaDt4rFXSBAQBASBdCEgk7p0IZ3H8iEGHE4pWDGP98dALD9RXim7yBm81vXo0cMEqx82bJjx1gjGkYh08doFvzdv3jzsTGUkXs57xFvkjCVnbjmjGingOOm9yunkHetzNugSYRzef/99NWPGDBPCAPzwWorH086dO6sSJUoYJ075rV9KlS7Fqu9Efsu0nJy5bNWqlfGG2qBBAxOvkrAXNWrUMGfG2anD8VWm5SS8C+2emIvosJAgIAgIAulCQMwv04W05CMICAKCgCAgCAgCgoAgIAgIAoJAChCQnboUgCosBQFBQBAQBAQBQUAQEAQEAUFAEEgXAjKpSxfSko8gIAgIAoKAICAICAKCgCAgCAgCKUBAJnUpAFVYCgKCgCAgCAiQaeHoAAAgAElEQVQCgoAgIAgIAoKAIJAuBGRSly6kJR9BQBAQBAQBQUAQEAQEAUFAEBAEUoCATOoCBJVYOXXq1FG1a9dWf/zxR4CchZUgIAgIAoKAICAICALZjcC4cePUFVdcocaOHWtCTmS3tCKdIJC/EJBJXQrqk07t559/TgFnYSkICAKCgCAgCAgCgkD2IkC8zZkzZ6qDBw9mr5AimSCQDxE4IR+WKWNFOv/889V7772nTj31VBNjyo8ghw4dUosXLzZxmM455xxVrVo1P49nNO2ff/6pli5dqhYsWKDuvfdeVapUqZjy2LLy3A8//KBOP/109cILL8R8Jogfbb5z5871JGcQeRZEHps2bVLr1q1TCxcuNPGa0GchQSCvIWD7C2L45bU+Oa9hXZDlpb/csGGD2rp1q/ruu+/Mu5CYjXmVatWqZeIGTps2TRFbUEgQEATSh4Ds1AWMNR3zJZdcom688UZfnA8fPqzmzZun3njjDcVkJy8Rq3LTp09XOTk5nlbmKOuSJUvUjz/+aP54Ph1kMR4xYoQnOdMhU37M4/fff1djxoxRs2bNUseOHUuqiOjJzTffrAYMGKCOHj2aFC95uGAj4FeXbH+RF/vkRGv6n3/+UQTxvueee8TaJFEQfT5Hf8m7f8WKFWr58uVJ95k+s/ednD79iy++MIu4kR4+cuSIKQdB4U866aRISeSeICAIpAgB2akLEFhWdMePH69efPFFs1vnh/UZZ5yhnn/+ebVv3z4/j0VMi8nDqFGj1BNPPOFbjogM49y86KKLVIsWLdT3338fJ+X//ZmyNm/e3HxBzl9++cXTc8kmIt+mTZuq1atXJ8tKno+BwF133WV2Nrp27RojlbefOJvRpk0bdccdd8Rc9U23znuTPj2pCnLZ/SDsVZcszyD7ZMsz268nnniiqly5srrhhhvUpZdeGlNcrCy01qpMmTIx0+XHH4MsO/0lfzt37jQ7dtmK16JFi9R//vMfhe8ArIpmz56dS1QWQoYMGaJuv/12dffdd+f6XW4IAoJAahGQSV2A+BYtWtSscgbIMiFW+/fvV6z+CQkCeR2BwoULq8aNG8ctRkHW+YJc9riK4UjgVZccjxS4j8cdd5wqV66c+YtX+PXr16srr7wyXrJ8+XtBLHvx4sXV6NGj1caNG6P2ySwKsMArJAgIAplBQMwvM4N7ynLFlPHtt99Wv/76a8ryEMaJI7B79241dOhQ9ffff4cxwbEOZxAsYQb122+/2a8F9oopz5YtW2KaJAep8+w8sBKdLpPgZCs2yLInK0u2P+9Fl7K9DOmQb+/evWbgHi0v2gjnp996661oSXzdz0t9XdBl9wVUhhNjSnnuueeaiTzWOUKCgCCQfQjIpC6DdbJr1y71yiuvmE6yRIkSxgUwLzg3Mcjs3bu3KlasmGIlleubb74ZNjFgkoBJIyZD3bp1Mw5bTjvtNJMecxpMO9z0008/qQYNGqgzzzxTkf/gwYPDeLrTe/3OWbnHHnvM8IV/MuaVvEQ5r/fAAw+YsmCmwnktnBi4iXv8RhpwwmkLz8IjGoEL+JCeP8xB/RC7JOAG9jhgwSzn4YcfNmUntAUYW+rRo4ditwAHIpZsvXEwfvv27eb2nDlz1Mknn2z4cpaMM5qvvvqqOXMBrqyOf/rpp5aFr6sXXaIMyIpuEprj9ddfN3midzgCYnDsJGcdoUtPPfWU2rx5szOJ78/UA7zY/QZPzqq6yWIXlM7D75lnnlEPPvigmjp1qtE5zD7RZ8oN7k8++aSZ9Lll8fLdq356qaOgy+6Un0k0dQj+lBk9+OCDD0wfwdWSs95pO5HaJvrfp08fs3pPO8NEnTPDnTt3NgNEZ/gXdM62Qz5DtAV775ZbbjHP2/y9Xr3okuXltU+26eNdOX80Y8YMVb16dYMnmFJmzvdZcvdBtuzO+/Qv1Lklr3Vk08e72rwwUb366qvVxx9/HPER6uP444835tCcA+Psna2fSH0nOj9s2DCjO5Sd9wHnxiylsq8jD3aVMLlnMsIf5wWpY0t++jq/Zbd5BHX1oks2L/poQgrwXqd+eH/ilZt2XalSJbHksUDJVRDIbwhooYwgsH//ft2iRQs9cOBAfeDAAfM3ZswYXbp0aT1y5MiQTNu3b9c1a9bUQ4YMMWn4YdeuXfrpp5/WQ4cODaVzfpg9e7Zu1qxZKL3zN/t51apVul69enrBggX66NGjeu/evbpv3766bdu2+uDBgzaZ5+uOHTt0pUqVdPfu3fXw4cM15Tt8+LAeMWKEkZ9yRCLKyjPRCEwqV66sFy1apI8dO6a3bt2qmzRponv37m342+eQuVu3buY30pB2yZIlumzZsnrx4sU2mcEEbMDI0urVq/VLL71kcLX3vF7Jd/r06absnTp1MvUJltzv2bOnbtOmjT506FCIHXi46wac2rVrF1bvYILsK1as0Fu2bDH8SbNv3z49efLkXDxCGcT44FWXNm7cqPv166dr1aql27dvH9KR5cuX6/vuu08vXLgwLJeZM2fqRx55RK9cudLo0vr1641+VqtWTaMXyRAy165dW69ZsyYmm6B0nnxKlSql+/TpY+pw0KBBuly5cnrWrFl69+7dps04dSemUI4fveqn1zpysDa67NYp5+989treyb9u3bp64sSJpn3Rjrt06aJbt26tp06dGlb3Xtrm//7v/+oZM2boYcOG6bvuuss836NHD6MryHTrrbfquXPnGnFps+h206ZN9V9//RUqAvpm6yN0M4EP8XTJa5/sJ2swqlq1ql63bp3pk+hrv/rqK12lShXTl1le6MfLL79ssAYHS/RNtHtks+Snjuwzfq70UbH6ZHjxznL3o+486NeoN94r9ImUnfcNfQVXS6no6+C9dOlSfffdd4d0GZl5R4Cn7ZP99nXw9VJ2WzY/V/rKOnXqRO3rvOoSeZKWdmTfhfPnz9cVKlTQkyZN0uPGjQvTJz8ykta+6xPpB/3mJekFAUHAHwKyU5ehWfpXX31lVqwbNWpknJkQBuHxxx9XDz30UJhEOE45cOCAWRElDYR77fLly0fcvQh7OMoXTLbw6NaqVStzoJmVV1ZRmzRponbs2GF2hKI8Gvc2Thvq1atnwhSccMIJZpcFr4XOlem4TP7/BJgffvLJJ2al/7bbbjMrjhdffLHq0qWLcf3s5MmhbQ5yd+/eXZGG1Ul2xaBYu0bsRE2cOFF16tTJ4OpVNpuOHTV2KNjRuP766w2mYMl9Vucpg9ObaSQXz+DEbpObWF3Fiyr1TtgHcC1UqJBZSScPv+RVl4oUKWLKhN61bt06pCM33XST0bs1a9aEssacFHPfZ5991siKLrHSj7OgU045JZQu0Q/w4y9Z8qPz1n09dQjO7EaULVtWnX322WbnMBFZvOqn1zryI4OfshOKAl2rWLGiQi/5TB/F7gZt0IYr8do2zzvvPKMzOE1Ax+n3nn76aaMr7PziIZXdUIg2W7p0afMZE0BL5IU81EcyFE+XvPbJfmSg7wEz2gTlQwa+046dTrEoG2VkB452Z2nbtm2qZs2aph7sPa91ZNP7vUbqo/zyID11y9nu5557zrQjyo4DjWbNmpmzWU6rlKD7Oqvz7LxXrVrV6DL9KO8ELEfsrqefvi4RDIJ8xqsuUTZixPG+sO9C2hUWD3v27MmlT0HKKLwEAUEgswiIo5QM4M/LjEEeAx3n4JyXvvuFes011xj3wVZMzP148WPW5E5r08S7ct6OiQa8nYQsvOQ432UHb87fvXzGtIPBoCUGwpiAYHKIWZsfYpJ24YUXKnfMHswRmUgR04dBApPGb775xgwenbb+l112mZoyZUpUD6CYuTEh4WA3g6xk6aqrrjIDN8vHDuLsd79X6hceEOcZkh3U+tUl8DvrrLNCYkfSTwZI6BJhB5yUbNmdvIL47EfnGXxa3MmbwWCibY3n/ein3zrygo2fskfih/MDZ5smjde26eTHAgDt1bZRMHa3O0zkMAFctWqVGZBivkcMr/vuu8/JKvDPfvpkP5m3a9culByTOMwc8RzoXOixCVjAYfKMEw76TPQGc1XMNeNRpDqK90wqf8c0F3N06tvdb9FPYmrOxMu6vA+6rwNDJpQ4fXG2ZUwxGzZsmGsRzUtfl0q8vPD2o0uR+HEcQ0gQEATyNwLJL4Hnb3xSUjpe1qzGsormhXj5vfvuu2Yiw8SIiQgvrUSJ3Tjs7VlF54Vn/xjMch7PfWbKTz7uwS+8uZcITwKa41bb7lBaOeDJwIABD6uSDMgYDLknqaRj0OgekMKHVfKBAweayXUyWFqZ8so1aF1iZxaM2dHJZkqlzscrt1/9DLqO/JSd3WbyZ5GENkv7+vzzz9W1115rdiptWb22TZueKxM2Fo1iEW2WPo5dHvpJzlKiX87FhVjPJ/qb3z7Zaz5MbsCKs0wsfDCp+Pbbb8N24ywvJrsspn399dfmHDA7M0zW2Dl2ktc6cj6T7s/0Cywm/Pvf/w69X6hb/giXgE4mG8MyVpmYNKM3LCo6iUlkJKsIZ5ps/exVl3hfYsnDGUcmtjzH7i67d0yyhQQBQSD/IiCTugzWrfuFE0kUzANxzoAZDmaCrH6+8847xrlHpPRe77GjxouVDt/9h9lGXiQveNpyffjhh8ZUErNLvLixa5ffKVW6BO4MPrOdMq3zXvQzVXXktews9GA2jMMhdskZgGMZgMmce8EmVfVtnQYxKcCpRsmSJVOVVS6+Xuoo10NRbtCvgiPmh/Xr1zdm7Z999pkxTQZbNzHhqVChgjFRpb9n8hep7NlQR27Zo30fOXJkrvcLuHz55Zdmkh/tuSDu55V+yUtZ/eoSesTkjiMdLNZiHs/xChYEhAQBQSD/IvD/7OTybxmzrmS8vBkgMViKR5MmTTLmh0w+Iu04xXs+0u+sYEKYcrJ6nkpiBZzV/vPPP993NryA8LDGToc107FMMN9h1Z8XF79jWoKnMzxeuolycr7LiV/Lli3NoJXysysxfPhw9cILL6R84OqUwS1nqr+nQpfQYwbf1HE2m/ekU+fd9QhGXvUzFXXkp+yYPXJmEu+HsSZxXtumGwsv39mZwusp3gZZbEm16SUy+emTvZSBNEzM8DiYk5Ojbr31Vk+PUW4WSDDPZ6cOb7du8lpH7ufS+Z3+mvNr1qNvOvMmL/p7dqnYdXb3S+xAs5OYl3bs/OgS71y81LI7jDWKkCAgCBQcBGSnLgN1zQsHpwPLli0LM0vkzAnnLZyE+REvn2iTAUxYOHcSi/h98uTJoSQ49WAVk3Mxbvr+++8Tchtu+TgP+XMPM0fOXeFowi/hQIHzNDhKcBI4MdnDsQDEAAJnFkzOnM4H+I2X+oQJE4wpl5OHHbCCKyuYmDzh3jrVxNkNXrqsvFpCZs4xppqC0CW3jEys0WcGoE5auXKlop4yRenU+Xhl9KOfQdRRsmUnDEi/fv1Me7ITdncZvbZN93Nev9O2mViiW6k2vUQmP32y1zLQrukP3ZMK5/PUldMMkUUmzguz6ERfYSfkzmf47KWO3M+k+vuKFStC4WvoX+nzeZ+4+wH6ZCa79IOpIhzTsODnfp+SH+8OZ6iZIGRwlj0Ifm4efnWJSSsTOoKF41CMOnDqmZt/Jr6zYFOnTh1zZhQLBSFBQBBIHgGZ1CWPYUIc8Mi1adMmNX78eDOx40VHB8wAgODUmFpCvJwISo1NPETnjEkPZjzsjnAezP3iYjDACim/0ZGz6utclcQhCl7B+vfvb+JxsXLJH7G5iMnlPptmMvbwj4ERZeCFyaSFFxGr1OzSFS9ePCIHXuzspJG/m5h81qpVS7Vt2zbEk9XXnj17mpVvJsaWMDeBF3Hd7CCCKy82TMjsYX3kIh27e5bIp3Hjxqpv374mLpu97/WK7IcPH841SCEv6og/S5hOrV692gx2qBswYpeQMyAMYql3KyNy8pl09jN8GChSNmcZLP9YVz+6BG9nnvC1ciEznyHOAVFH4MwEgPsEJsYxDjrI5IBV5kSJsjNAcS8WuPkFpfO2jLZ8YGBxoJ7RVcrvl7zqp586sjIEVXb4cXYO80vrROiJJ54wO9rsgDsXPfy0Tfi6cbWyR7tyZhaHSEGeAYqnS1775Ggyu++z40ifiKkhkzf0B3NS+iisNLiHx036fkvWBBPzTOsV1P5mr17ryKb3e0Xfo/XJlhcTJnYVMRGlHPRHfHaalXKu64ILLjD9Nf02xBXPy5wvZOJn9cK2saD6OrDH5BVvyBxZAHv++MyEGKc0lrz2dTa9l7LbtH6usfTTjy6Ba926dc1iJ7vd7du3V2XKlDHv4a5du4bpmx/5bFoWnujT6eOp+2SJCX46FjWTlVOeFwTyBAL+IiBI6iAR2Lx5s27UqJEuVKiQrl69uomn1r9/fxPrjBhOELF+iPFUvHhxRtEm5hFxwTZs2KDLly9vYolt27YtTCziHE2YMME8U6RIET148OCwmG428bJly3T9+vVN/qSzMfPs736uxK5BTmLoEdcKfkWLFtU5OTm54uURC6xkyZKmPJTJ+eeOfUNso2nTpumKFSuadHfeeaeJwQMubtq5c6fu1auXyddiRQwsG/fJxtex+dl4gE55kCteTDRnvtF4Ug6bj5Ons26od/AnhhXYE9+Pekcu+yx8iOPEd+IArl271lz5Hi+WlFNOPnvVJWf+5EkZbWwmK5czb/gSFwkdtXpEjKiWLVvqKVOmmNh6blnifXfKYPOMVWYnronqvLMuicFFfEOrp8hjZXLWZ7xyOH+Pp5+k9VpHTr5BlB1+R44c0QMGDNDz5s1zsjfxxbhHDC3atyUvbdNi5qzDWPVoeSMLsTn37NljbyV89SODlz7ZjyDEbHT2sa+99pppT2+99Za++OKLTbux/ZPlS2w/YvnR5tzkt47cz0f77tR9Z13F0nXi5REz1L6/6MfcRBnefffd0PvLvudsOmfdBNnXwR9ceVc++uijoXcccfOI9WnJmb+Xvs4+56XsNm28q+3fnbjz2R170qsuUQ+dO3fOFW+WWIcdOnTQY8eOjSdSxN+d7zSnrM53QcQHY9ykfTds2DAs/mWM5PKTICAIxEHgOH7PE7NPEVIQEAQEAUEgZQiw24JTj1deeUURQ85J7Nx17NhR9erVK+XncMmXHR12VPKq0yYndkF+zqY6CrJcwis4BIjtijUI8VzZ+XXSRx99ZHZIs6VdISfOdOhz2AEVEgQEgeQQEPPL5PCTpwUBQUAQyBcIYJaNt0W8wXKOlfU+/oitNmLECGNKl8rzbZghMsDjfOb8+fOjmh/mC7ATLESm6yhBseWxNCLAsQTMLnG6ZE30udKmCE8S7ShEGkU0WXGkhOMGL774okzo0g2+5JdvEZBJXb6t2uQKRgy4W265JVeMIVb+3H+8QPIT5ZWyi5yZ07oePXrkagfudsH35s2bh52pzJzE8XPmLE6rVq3U/fffrxo0aGBcoeMOvUaNGuZMLjt10Rw2xecePwWxJjlfytk2zqJxdsxNmdb5TNe71zrKtJzuekv2e6br3av82SAnIUHef/99NWPGDBPCgH4Ib7VDhgxRnTt3NoHts0FOZOK8H15ShQQBQSAYBMT8MhgchYsgIAgIAoKAICAICAKCgCAgCAgCGUFAduoyArtkKggIAoKAICAICAKCgCAgCAgCgkAwCMikLhgchYsgIAgIAoKAICAICAKCgCAgCAgCGUFAJnUZgV0yFQQEAUFAEBAEBAFBQBAQBAQBQSAYBGRSFwyOwkUQEAQEAUFAEBAEBAFBQBAQBASBjCAgk7oAYSeWU506dVTt2rXVH3/8ESBnYSUICAKCgCAgCAgCmUJA3u+ZQl7yFQQEAa8IyKTOK1I+0o0bN079/PPPPp6QpIKAICAICAKCgCCQ7QjI+z3ba0jkEwQKLgInFNyiB1/y888/X7333nsmkCZxlvzQoUOH1OLFixXxY8455xxVrVo1P49nNO2ff/6pli5dqhYsWKDuvfdeVapUqZjy2LLy3A8//KBOP/109cILL8R8Jogfbb5z5871JGcQeRZEHps2bVIEll24cKGJ04Y+CwkCgkDBQsD2t+nu51OFcjLv91TJJHwFAUFAEHAiIDt1TjQC+Lx161Z1ySWXqBtvvNEXt8OHD6t58+apN954Q/ESzEv0119/qenTp6ucnBx18ODBuKJT1iVLlqgff/zR/PF8OshiPGLECE9ypkOm/JjH77//rsaMGaNmzZqljh07llQR0ZObb75ZDRgwQB09ejQpXvJwwUZAdCm99Z+pfj6ZUmJiycLs33//HZFNou/3iMzkpiAgCAgCASMgk7oAAWWXbfz48erFF180u3V+WJ9xxhnq+eefVzVq1PDzWMS0TKw++OCDqC+miA8lcfOiiy5SLVq0UMWKFfPEhbI2b95ctW/fXtWsWdPTM0EkIt+mTZuqu+66Kwh2wiMKAuBL3QaxQ3fFFVeoNm3aqAcffFD961//ipKjMpP0dOp8VEHi/JDuthlHnAL1c37TpVRUXpD6mal+3i8ulLlTp07q/vvvN+8wLGYiUTLv90j85J4gIAgIAkEjIOaXASJatGhR1bFjxwA5JsZq//79it0SIUEgryNQuHBh1bhx47jFyCs6n1fkjAt4HkyQ33QpFVVQEPXz5JNPVm3btlWtWrVS7777rmI3LhJly/s9kmxyTxAQBAQBEJCdunymB5gyvv322+rXX3/NZyXLH8XZvXu3Gjp0aK5dVBzrTJs2LVTIf/75R/3222+h7wX1w5EjR9SWLVtimiQHqfNaa4UJVipMgoOUs6DqQzLlzk+6lAwO0Z4tqPp53HHHqbPOOktxZu6aa66JBo/cFwQEAUEg6xGQSV0Gq2jXrl3qlVdeUVdeeaUqUaKEGjt2rGIw7yYGmb179zamIbyAMHN88803wyYGnAHApBGTl27duplzAaeddpoifeXKldXOnTvdbNVPP/2kGjRooM4880yT/+DBg8N45nrA4w3Oyj322GOGL/x/+eUXj0/mTsYgm/N6DzzwgCkLpn2c1+IQvpu4x2+kodw4beFZeEQjcAEf0vM3atSoaEkj3mdlG9zAHgcsOH55+OGHTdkJbQHGlnr06KHYLcCBiCVbb9ddd53avn27uT1nzhzF6jF8OUvGGc1XX31VrVixwuCKvnz66aeWha+rF12iDMiKbhKa4/XXXzc6it5x3oTBsZOcdYQuPfXUU2rz5s3OJL4/Uw/wYnUcPCOtnlvsgtJ5+D3zzDPG1HPq1KlG5+644w5z7pNyg/uTTz5pJn1+CuRHTnC3ushnCH2w92655RbjTOmLL74I1RHtC7Ni8ELnP//884hnGYNu7150iXp0y06ZnPcpnyV0i36Q/pDnaPd4G0SnKlWqlJAFQn7SJYtTvL7OT7/kRz9t/kFfveiSzZNFHvQBfadN0k9heo3OcBUSBAQBQaDAIqCFMoLA/v37dYsWLfTAgQP1gQMHzN+YMWN06dKl9ciRI0Mybd++XdesWVMPGTLEpOGHXbt26aeffloPHTo0lM75Yfbs2bpZs2ah9M7f7OdVq1bpevXq6QULFuijR4/qvXv36r59++q2bdvqgwcP2mSerzt27NCVKlXS3bt318OHD9eU7/Dhw3rEiBFGfsoRiSgrz0QjMKlcubJetGiRPnbsmN66datu0qSJ7t27t+Fvn0Pmbt26md9IQ9olS5bosmXL6sWLF9tkBhOwASNLq1ev1i+99JLB1d7zeiXf6dOnm7J36tTJ1CdYcr9nz566TZs2+tChQyF24OGuG3Bq165dWL2DCbKvWLFCb9myxfAnzb59+/TkyZNz8QhlEOODV13auHGj7tevn65Vq5Zu3759SEeWL1+u77vvPr1w4cKwXGbOnKkfeeQRvXLlSqNL69evN/pZrVo1jV4kQ8hcu3ZtvWbNmphsgtJ58ilVqpTu06ePqcNBgwbpcuXK6VmzZundu3ebNuPUnZhCRfgxnpzoLfXbtGlT/ddff4U4gLmViZu035dffllXqVLFtFkw51muVatW1aNGjTLfLYOg27tXXSJ/2m758uU17cwSsr722msa3XES7Z2y2zY8f/58XaFCBT1p0iQ9btw406840/v5nF90yUtf57dfsjjG00+bzu81Vj/vR5dIW7duXT1x4kTT//Oe6dKli27durWeOnVqrr4pETnd/bNfHpJeEBAEBIFMISA7dRmazn/11VfGpKxRo0bGqcqpp56qHn/8cfXQQw+FSbRv3z514MABdc8994Scr+CAonz58hF3L8IejvIFMxu8bHKG4Pbbb1fHH3+8WfVs0qSJ2rFjh9kRivJo3NscOq9Xr54JU3DCCSeYXRa8FuJ5zi9hfvjJJ5+oPn36qNtuu82s3F988cWqS5cu6rvvvgvjOXv2bLVo0SLVvXt3RRpW+dkVg2LtGrETNXHiRHNQPhHHHuyosTPIivH1119vMGUFmfvVq1c3JpROb6aRnH2AE7tNbmKXAi+q6AZhH8C1UKFC6uqrrzb15U4f77tXXSpSpIgpE3rXunXrkI7cdNNNRu/WrFkTygpzUsx9n332WSMruoR8OAvyG9YjxNTxAX78JUt+dB49IKQIdUhd0vbKli2rzj77bLNzmKwssZ5Hb0uXLm2S7N27N5SUtlCxYkUjEzdvuOEGVaVKFfM7WIM5z3Lt0KGDGj16dGh33k/ZQxnG+eBVl2DDbityOc2J0S3M3Wg7ltgxmjlzptFz24bBgp3aPXv2GKdKtINEKb/okpe+zm+/lCimQTznR5cIlYIO0BboN/nMOxSrF94R8cLpBCGv8BAEBAFBIFsRSH60lK0ly2K5MLHkxXz33XeHDc4ZlLkH/dj4Y2rFIA7CrGb16tVhZn1+i8p5OyYa7vMDDGAZ0CcTOB0TKV62lhgIYxbjNDm0v8W7Mkm78MILFaaJTsIckcEgEzuISeM333xjJhx44rR02WWXqfvNedcAACAASURBVClTpuSaKNvfMflhQlK/fn0zWbL3E71eddVVZmBtn6c+k5mQoAvwgE466aTQgN7y93v1q0vgx1kTS5H0E9M/dImwA05KtuxOXkF89qPz1JnFnbyZVLvbZRAyReNx7rnnKjw1rlq1yiTB1G7Dhg3q2muvzfWIu45IYPXQmqz6KXuuDKLc8KNLDLzp62ijNizF+vXrjVkxk494hBl5NpEfPIPWpUT6OqsPFsNsa5t+dMmWwXk98cQTw945zt/ksyAgCAgCBQkBmdRloLZ5MbNSzWq0F2KlHa9cTGRw7c5EhEFRosRuHOdWzjvvPDN45SVvX/Scx3OfmfKTj3vwC1/uJcKTgOaXXnppaIfSygFPBiq4mGZ1n0kyK7XuSSrp2NlyTjItD1aHBw4caCbXyWBp+eWVa9C6xM4sGCezg5IO7FKp80HLj97SzonzR1/B5AyMnRPsWHkyUSJGmN0hTlXZ/egSu53Lli0zu+acwVywYEGuhQAmz1ggcAYO772kY2eG3TssCrKFUoWnl/Il0td54ZvpNF51CWsI0rJAwDuF/p8zpCx4sIAoJAgIAoJAQUbg/22pFGQUMlR2Ly8hzANx3lC8eHFjJnjBBRcYaRn4JOOAhB01eLArkF/IC562rB9++KF66623jBkPVwYLmIPlZ0qVLoE7q+XZTnlJ563jHHaFli9frkqWLJkUvEGX3a8usfPIriKOizAxx0FRpEWtChUqGGdDmKKzE4+Z3X/+8x/TPpMCIOCHg8bTr3h++jq/vNOd3o8usRCJWTsOsRo2bGgWO/7973+rF154Ia276enGSPITBAQBQcALArJT5wWlgNPY3Su8eMWjSZMmGfNDgqPaCV28Z+L9zqo/hClnqomdBlZTI50Zi5c3Ey3ObEXyCIqnSExFrWkcJlobN26MyJJyuncKW7ZsaUw78RbIDt/w4cNDpmERmQR0M9KuYUCs47JJhS6xC8vEgzrOZkqnzgeBA+f6OIuGZ8hoppfR8qHNQHbBJhVl96tLtNNy5cqZnXF2WW699VZjUuwsA30F3gvxbPjtt9+anbqvv/7aBIWmz8wWSgWeXstGe/Pb13nlnal0fnQJk2TO9H788cdmYYA+H8/A9vx0psog+QoCgoAgkA0IyKQuA7WAAwkOdWOO5JxsMBhbvHhxmESYXjEhijYZOHbsWET3/k4mnMmZPHly6BZOPVjp5cyam77//ntj1ui+7/U7ZqVOwsyRHUUcTfgl3MkzoHU6WIAHOM2bN8+s4vOd82aYdzFYJD8nYaozYcKEXBM2ayYKrjiIYfCIK/9UE7sVDF6dYRaQOZlzjF5lDkKX3HkxsUaf3c5oVq5caerJnT5d39Op88mUyS2nkxe7VAxewTea6SXtzdmH8Dxtg12wyy+/3LBLRXtPRJcwoWTXEQsB9xlMW27MeTGLxtELzpVo6/RxmSR3HaUCT6/lS6Sv88o7Ujp32SOlSfaeX11it7dfv36mv8/WBSXCcLA7zTEHZ1+fLFbyvCAgCAgCsRCQSV0sdFL4W9WqVdWmTZvU+PHjzaCMyQcDGVZhCU6NR0YIr3EEpeZsCcQgB9OTzz77zOyOcB7MPRFk4sBOFr8xIMIpi3OnDIcomHT2799fEY+LQSF/fCbGnPtsmsnYwz8Gn5SBeFi8yJis5OTkGLNGzEcjEROcSDtppGXwVKtWLdW2bdsQT87a9OzZ06z0MzG2hNkWvIjrBkYQVwaIOJmxDhmQi3TO3T/yady4serbt6+Jy2Z5er2CHWeY4Osk8mIHy7mLhfkQjm6YPFM3YMQuIeefGMBT71ZG+PGZdPYz/BnIUzZnGZz5RvvsR5fg7cwTnlYuZOYzhGMa6gicGWBxn/NSOMZBBxl8bdu2LZpIce9Tdgb67sUC94NB6bwtoy0fGFgcqGd0lfInSvHkdPLl3ChOgWKdJ2NhiPO2yARW9BWDBg0ypmn2nGMq2rsfXbJlYpBLWfBoGcnqgIWWunXrmkUadijbt2+vypQpY/qPrl27Jh0QPr/okte+zk+/ZOvIj37aZ7xcaUPR+nk/usTZOcwvrZOrJ554ImRxkeyiHBNYFiDZDfRiRROv3LzTOQ9K/yUkCAgCgkBaEMhULAXJV+vNmzfrRo0a6UKFCunq1aubeGr9+/c3sc6IKwURw2zYsGG6ePHijKJNDCpiO23YsMHEfiKW2LZt28LgJAbUhAkTzDNFihTRgwcPDovpZhMvW7ZM169f3+RPOhszz/7u50o8MuQkhh5xg+BXtGhRnZOTkyteHrHASpYsacpDmZx/7hhgxNCbNm2arlixokl35513amJZgYubdu7cqXv16mXytVjNnTs3FK/LxtKz+dl4gE55kCteTDRnvtF4Ug6bj5Ons26od/Bft26dwZ74ftQ7ctln4UPMOr4TB3Dt2rXmyvdY8f2cMtrPXnXJmT95UkZiKRK/ycrlzBu+1Ak6avVo6dKlumXLlnrKlCkmtp6VwevVKYPNM1aZnbgmqvPOuqSsxDe0eoo8ViZnfXotj03nVU7SHzlyxMSn3LNnj3087Gpjis2ZM8f0H+BDP2JjOoYl1loH2d696pJbhtGjR4fFiHT+Tjvo3LlzrjiZxCLr0KGDHjt2rDO558+23px6lNd1KdG+Llq/ZMH0o5/2mWhXZ7/qxh45LHnVJdrDgAED9Lx58+yj5so7gnt16tRJKNaou2+zsibTzhGMeJPuOKVhgssXQUAQEAQCRuA4+KVl9iiZCAKCgCAgCHhGgF1pTM2ITxiJ2M1id5eYk5xZy+tETEp2sYlD6T5D99FHHxlHGNGwyOtlF/njI4B1wnPPPadeeeUVVaxYsbAH2Lnr2LGj6tWrV+gsaViCNH9hlxSrEY4dJHL0IM3iSnaCgCCQTxAQ88t8UpFSDEFAEMj7CGCyNXLkSHNGcf78+YpzpdEIk0vMfhlA5gfCnJqJKo4zrGkxV3DAbX00E+78UHYpQ3wEMCPGCyzeijlnzXo0f3hSHTFihDHpjXb2ND734FLQJjlCQdslPqOQICAICALpQkAmdelCOo/lQwy4W265JSyOHavnkf4YiOUnyitlFzkzp3U9evSI2Bbc7aN58+ZhZyrjSUy8Rc5YcuaWM6qRAo7Dg/wJDTBs2DDjVRJdSIayQZcI4/D++++rGTNmmBAGYIkH3CFDhqjOnTurEiVKGCdO+a1fSpUuJaMPkZ7NtJycuWzVqpXxhtqgQQNFYHf+atSoYc6Ms1OH46tMy0l4F9o9MRfRYSFBQBAQBNKFgJhfpgtpyUcQEAQEAUFAEBAEBAFBQBAQBASBFCAgO3UpAFVYCgKCgCAgCAgCgoAgIAgIAoKAIJAuBGRSly6kJR9BQBAQBAQBQUAQEAQEAUFAEBAEUoCATOpSAKqwFAQEAUFAEBAEBAFBQBAQBAQBQSBdCMikLl1ISz6CgCAgCAgCgoAgIAgIAoKAICAIpAABmdQFCCqxcurUqaNq166t/vjjjwA5CytBQBAQBAQBQeD/Y+9MwG6q3r+/6t9cGpRKk2ZN0qRCA9HFi5SiDGVIhlARoWQuQ4ZIRZII0asfJRHKUIZMmadKpkhCyE9k2O/1Wf93nfbZzz7n7H3OPtPz3Pd1Pc/eZ++11/Bd95rvQRDIqwiMHTtWXXbZZWrMmDHalUNexUHKLQgIApERkEVdZGzifkPn+/PPP8f9vXwoCAgCgoAgIAgIAoKAHQH8WM6cOVMdPHjQ/ljuBQFBQBDQCJwgOASHQIECBdR7772nTj31VO1jyk/Mhw4dUkuWLNF+mM455xxVuXJlP5+nNexff/2lli1bphYuXKjuu+8+dfvtt0fNjykr333//ffq9NNPVy+++GLUb4J4adKdN2+ep3wGkWZejGPTpk3qxx9/VIsWLdL+muBnoWARWLNmjfrll1/UTz/9pBo1aqT7nGBTkNgEgexGgH5ow4YNatu2beq7777TYwy+ELOVqlWrpv3xTZs2TeGzT0gQEAQEAScCclLnRCTB3wwgF110kbrhhht8xXT48GE1f/589eabbyoWO9lE7B5Onz5d9e/f39MOImVdunSp+uGHH/Qf36eCDMbDhw/3lM9U5Ck3pvHbb7+p0aNHq1mzZqljx44lVET45KabblJ9+/ZVR48eTSiu3PQxC7ohQ4aotWvX5qZi5dqy/PPPPwrn2Pfcc49IcaSolumHGFNXrlypVqxYkXBflOxs01d++eWXenPULa0jR47ocuBs/aSTTnILIs8EAUEgjyMgJ3UBMsC6devUuHHjVMuWLX3vnJ9xxhnqhRdeUPv27Us4R4hmjBo1StWsWdN3PuJJ/MILL1RNmzZVc+fO9fQ5ZW3cuLEOSz6ZoKaCSLdhw4aKUw6h5CFw9913K07nOnXqlHAi6JC0aNFC3XnnnVF3p1PN8wkXLMEIKlasqPbs2aNPIPxExcm4ZVmqePHifj6TsAkicOKJJ6ry5cur66+/Xl188cVRY8vLdRRk2emH+Nu1a5c+sYsKehpfLl68WL3yyisKnXykdWbPnp0jN2xIDh48WN1xxx2qRIkSOd7LA0FAEBAEQEAWdQHyQeHChfVubIBRxhXV/v37FbuUQoJAtiOQP39+Vb9+/ZjFEJ6PCZEOsH79enX55Zd7CyyhAkPguOOOU/fee6/+ixVpXq6jvFj2IkWKqI8//lht3LgxYl/HpgAbp0KCgCAgCERDQMQvo6GThe8QZXz77bfVr7/+moW5z/1Z/vPPP7XY3N9//x1WWAzroCthCHGtrVu3mp959orI0ZYtW6KKJAfJ85xisWOeKpFgZ8UigvX111/nEMFCH/Szzz7Tpw7Ob7z8plzovA4YMMBLcAmTBAT27t2rJ+6Rog66jrKpDwm67JEwzsTniFKee+65erMFqRchQUAQEATiRUAWdfEiF8B3u3fvVq+++qruzG+++WZtqpiB2ElMMnv06KGuu+46xY4v17feekvZFwbcI9KIiGHnzp21wZbTTjtNh0fsBxEUJ2FkoU6dOurMM89UpD9o0KCwOJ3hvf5GV+7RRx/V8RJ/IuKVDPbo65UtW1aXBXEa9LWY5DqJZ7wjDDhhtIVviSMSgQv4EJ4/xEH9ECdE4Ab2GGBBfOihhx7SZce1BRgb6tq1q+LkCQMihky9ocC/Y8cO/XjOnDnq5JNP1vGiS4aO5muvvaZ1Q8CVk5ZPP/3UROHr6oWXKAN5hTdxzfHGG2/oNOE7DAGx0LKTvY7gpaefflpt3rzZHsT3PfVAXJx+gye6qk4y2AXF88T37LPPqgcffFBNnTpV8xxin/Az5Qb3p556Si/6nHnx8tve3ilblSpVtIgqYtsQ19tuu02nb+8H4IdTTjlF84NbOixA0cUlf7TjDz/8MKyO+P7444/XIqzoGKHX5cbvpH/LLbfod1z5bTA24eGL7du3q379+mmeJ25EzkmXiWmrVq30e2c+aZtDhw7V4Sg7/QJ6TvESi98ZM2ZoDImPP9obOpiGnG2bvEP257RbymiIDQT4l/jAkzbwwQcf6Hxz9UsmLcSIr7zySjVixAjXKLzWkfk4Fp7El6w+hDxwqoQoO3XOH/qC8LchP32I37KbNIK6euElkxZ9Hy4F4HfaBOMS1q7hmXLlyomEjAFKroKAIJAeBCyhtCCwf/9+q2nTpla/fv2sAwcO6L/Ro0dbxYoVs0aOHBnK044dO6yqVatagwcP1mF4sXv3buuZZ56xhgwZEgpnv5k9e7bVqFGjUHj7O3O/evVqq1atWtbChQuto0ePWnv37rV69epltWrVyjp48KAJ5vm6c+dOq1y5claXLl2sYcOGWZTv8OHD1vDhw3X+KYcbUVa+iURgUr58eWvx4sXWsWPHrG3btlkNGjSwevTooeM335Hnzp0763eEIezSpUutkiVLWkuWLDHBNCZgA0aG1qxZY7300ksaV/PM65V0p0+frsvevn17XZ9gyfNu3bpZLVq0sA4dOhSKDjycdQNOrVu3Dqt3MCHvK1eutLZs2aLjJ8y+ffusSZMm5YgjlECUG6+8tHHjRqt3795WtWrVrDZt2oR4ZMWKFdb9999vLVq0KCyVmTNnWg8//LC1atUqzUvr16/X/Fm5cmULvkiEyPPjjz9urV27Nmo0QfE86dx+++1Wz549dR2+++671r333mvNmjXL+vPPP3WbsfNO1EzZXtLGn3vuOWvChAmab2lz33//va5je3yUt2zZsmH8STQ//PCD9eijj4bhSdupXr269eqrr2p8iHPTpk1WlSpVLNqNk8iDk/edYSjjU089pctr3v3zzz9Wp06drOXLl+tH8ODnn39u3XXXXbpMI0aM0O0KnqdPIv3t27ebz3V5wZP+hbZBPul34Bmu8RDlq1SpkvXjjz/qtk6cU6ZMsSpWrKj7CBMneXr55Zetjh076nDmOW2e9kQ/ZQjsa9SoEaoj3vFd8+bNralTp+bge/Od1yttP1pfRzxe6oj+wgueyehDyOOyZcusEiVKhHAiz/S94Gn6Or99iNeye8XaHo4+iHYSqQ/xykvESdiGDRtqHmOMWbBggVW6dGlr4sSJ1tixY8P4yZ4HL/dmDLX3B16+kzCCgCAgCBgE5KQuPWtpNWXKFC1SVq9ePW3MBDcITzzxhKpQoUJYjjCccuDAAb27ThgIIxSlSpVyPb0I+zjCD7Oz//zzz2vFa3bx2Zlu0KCB2rlzpz4RivBpzMcYrKhVq5Z2U3DCCSfoUxasFtp30GNG8v8DIH74ySefqJ49e+oTDHZGCxYsqDp27KgNRNjjRLkchfMuXbroMITlVAyKdmrESdSECRNU+/btNa5e82bCsRvOySC7+tdee60CU7DkOScxlMFuzdTNFDU4cdrkJHaBsaJKveP2AVzz5cund/xJwy955aVChQrpMsF3zZs3D/HIjTfeqPnObnERcVLEfZs0aaLzCi9xIoGxIE6XEiXi4y9R8sPzxqUIdQjOnGyVLFlSnX322frkMJ68cApLfA888ICivinTXXfdpU+8zAkt8fLcjUc4dXezeIcrEUydc6LJt5wIPfnkk2rSpElhfOc1z5SREwf7CTOn0bQl0oDgQU4zOc3jJBO+hEcpH8aZzj//fC1CatLECio6vs8995zGk3xi8AFXDOgS2U8lzTexrrRpXKfAa7R14uQ3ebMbmyJPZcqU0SK88LMhThurVq2q25V5hhsO2hnhqSPu6Z85geIENZarFhNPpKtbvUYKG+25HzyD7kNMO+JEu1KlShon6h7+QCLDnHr66UOilTUV77zyEmXDRxz8zjgE3xUrVkyPcRgtcvJTKvIuaQgCgoAgYEcg8dmSPTa594QAkxgWIVixsk/OGSScA/9VV12lzRxjNQ1igoX1Rvuky1OitkDo27HQIG47kRcG40QcpzMhZEJkiEkioip2kUPzLtaVRdoFF1ygnL6FEEdkIYXvIYhF4zfffKMXHHadhEsuuURNnjw5x0LZpIsoIguS2rVr68mgeR7v9YorrtADvfneTDbNb79XeIE4ICb0TFATIb+8BH5nnXVWKEk3/mQiBy/hdsBOiZbdHlcQ9354ngWCwZ20mbQ626XfPLFQhN8QB7aLr7JYcG7k+ImbxebVV18d9gkbASwU2bCIh1hwLV++PKRXSF/D4sltUenkebC69dZb1apVq3S7RDQXsWTidPIv35JPFgp+qXXr1lp0lXoBT8qKzqB9A8XECR4szDDCAdFfIFrqxM2Et18xUGHvz+zv0nHvF8+g+xAwZIGO0Rd7G0EUE7Ffe38BPl76kHTgaE/TDy/ZvzP3bLgICQKCgCCQCQjIoi4NtcCkgl1jdvu8EJOegQMH6oUMO+QsRMwExcv3zjCcxqEXcN555+mBmcHZTMLRx7NPOp3fxvrtnPwSr5l4xfrW+Z5TCMx/M1G0E3EyITQ6PyySmbQ5F6mEY+febVLGbj56QSyuE8HSnq9suA+alziZBWNONTKZksnzXsrNhgk6XehCsph48cUXQyfizsWOl/hMGPuk3TyD75n808/EQ5w60+ZoF8SD7ptz0R4tXk6daY+0S/iDBTWnh+TL/sdGFfWCTpNfIl/0D+gykTcWFd9++63uV51xsdHDKRsGaPiOkxkWayy07cRJO+2DDSL6QE5mvvjiC11fbE5lAiULT69lY9FMe3fiwYLfTdrAa7zpDOeVl2gTSMig78vClu843eX0jk0LIUFAEBAE0o2ALOrSWAPOgdEtK+xAY5wBcSHEBNn1fuedd7RxD7fwXp9xosaEioHJ+Yd4STaSFzxNudhVRlQSsUssAnKKktspWbwE7kySM53SzfOXXnqpGjZsmD5h5kQcUUVEElkAZRKxyESsksUNYmV+NqAilWPkyJE5+hn6na+++kob2oj0ndtzvsMgEthxyo5z6c8//1yL/HKy7yQWkqVLl9Yi7/SjLP6KFi3qDKY3uRCZJm7iYdGJ4RTERJ2bVTk+TvGDIPH0m/Vsae9eyuWXl+AjFneoSnCij9g5agtsCAgJAoKAIJBuBGRRl4YaYJLBJIEJQyyaOHGiFj9k8YGuShDETiuEKGeyidMCdrzj2cVloERny03nBrEtJsYMsGCJCAwW2dyIcjpPH5s1a6YnbljI5ISPyXa8JxtuaUZ65nZqGCls0M+TwUtgz0mM0aUJOs9BxZdKno+WZ9o+i7sXXnhBiwsinjZ8+PDQJ0wUE+URLCOSTiI6jZxscRKOFcNIopehTDtuOAnjdIz0OcFB38quN+gI7vsnCzMsDvbv31+fnHjBC31ANh44mSd/bpPw1atXK/RFsVCJ1Ur6E6zOGt1c3xlNwgfJwNNPNqlTTqncRGbpY1MxpvjJb6ywfniJ8QELqJwOszHAgpDTX/RkaW9CgoAgIAikGwFZ1KWhBhgY0aVBb8W+2GABs2TJkrAcYcqdBVGkiQuiS0ziohHvMZxgCPEqdlvRWXPS3Llz9WTO+dzrb7sxAr5BzBG9K3R//BKnBRs2bMjhrw2cMM2OQQOIiQ7GLDhZsBtJ4B2Tj/Hjx+dYsJmdd3Blp5XBmQlssolJPJMDJgSGyHMieowmnljXIHjJmQYLa/iZibKd0KmintJFqeR5L2XctGlTDsNGnIhVrlxZLyDMxgVYGh6xx8v3nJo5ifZm70N4Dy+xiLHrlzq/M7855XJzOUIe2ERC1Cya6KWzvbN4o20anSvaGW2ffsXJD7RNFmd+N1NoL6QbTZeJ+reLdWJ2Hz1cNnMom1nkGxzMFZ3H3r17674kUzYr7HWUDDxN2b1cWeCzkeYcp/iWek9E19stfXvZ3d4n+swvLyH+itg+Bn4w1AVP2/ks0fwE8T1SJ9WrV9cuPuLVqw0iHxKHICAIpB4BWdSlHnOdIpbDmKjh44lJGRMcBgomKkOGDNGilgRkEMUpNbL7EIMI4kGIG3E6gt6Lc4Bl0sLkincMOOxO20/K0O/BelmfPn20Py7S5w/fXPjkcuqm6YQ9/GNCShkY2Fm0MGCym16gQAFVpEgR1xiY0LmdpBGYxSeW/fB9ZeJkl7hbt27aGAMLY0OIxRAXft3M5JErAzBiVEZviXwRzkyiTTr169dXvXr1isu4BNgdPnw4x+SUtKgj+ykWeowYumGSS92AEaeE6KpwQoCIrckj+eSecOae/DKhpWz2Mhgcol398BJx29MkXpMv8sw9xMKBOgJnJsE8x2AFhnHgQSbI7IbHS5SdiZRz8eCMLyieN2U05QMDgwP1DK9Sfr8ENvAx7Zh4IOJhUQN/sjEBmUUdOq/UMeVns4FTViZr8LcdT9o4fQg8ZsIiTozPNsPzOuL/b/CFxR6nDCx6iJ97N5FFFg8szFjYRdP9pa9iMk/a5I82hB4sCyhD6CERD+2W9gtxxbceC0azwWLCx7qiCwdOiG5SDvBE7w9skH7gGdaF7adJnKTQR1BWNovcCF1HxC8pB3rLiMcSntP8IDZ84KNIfZ3JDwumWHXkBU/Dx4Z3qR9zT1rx9iFgj8grVoZRBQB7/rhnQYxRGkNe+xAT3kvZTVg/V8oeqQ/xw0vwaY0aNXRbxbdemzZtVPHixfX41qlTpzB+85M/E5ZNN9o2fSc8nCjRt6RiszDRfMr3goAgECACxreBXFOPwObNm6169epZ+fLl076d8KfWp08f7esMP3IQPomGDh1qFSlShFm09s2EX7ANGzZYpUqV0r7E7D6h+Ab/OePHj9ffFCpUyBo0aFCYTzdTUvxO1a5dW6dPOOMzz7z3c8XHDvnEhx6+nYivcOHCVv/+/XP4y8NfUNGiRXV5KJP9z+mjB/9T06ZNs8qUKaPD4RsLX0Hg4qRdu3ZZ3bt31+karObNmxfyT2X8AJn0jD9Ae37IVyR/Rs70+B0pTsph0rHHaa8b6h388bUF9vj3o97Jl/mWePA3xW/8AK5bt05f+R3L55Uzv155yZ4+aVJG4z/L5MueNvFSJ/Co4SN8WTVr1syaPHmy9q3nzEus3/Y8mDSjldmOa7w8b69L/LnRHg2fkh+TJ3t9xiqHeU+94QMSf4i0C8oSiZfxl4YvRvgDTD/44AMLH4GvvPKK9c0334Tw/OyzzzSvzpkzR7cPwuNb0O6X0aRvrva48ScH70Ui6pD6cyPDD7RNeBfM+evbt6+rry7CDxw4MNSPkXa0fLqlaX+GL0R73/X6669rPh0wYIBVsGBBzY/whJ3++OMPq2vXrjn6I8IcOXJE533+/Pn2T7RPPZ7h54y+zS/ZecrOx9F4yEsdxcLT8CppBtmHUH5wZQx65JFHQmMHfvPwX2jInr6XPsR856XsJmysq+k37bhz7/QT6pWXaCsdOnTI4ccVf4Zt27a1xowZEytLru/tY4U9r/Y+1vXDKA/37Nlj1a1bN2HfilGSkFeCgCCQgQgcR54CXCNKVIKAICAICAK5AIH//Oc/2qofIrZO4mSwRYsW2ohTPKLVzvjS/ZtTSwyvvPrqckuQaAAAIABJREFUq+q6664Lyw4nd+3atVPdu3f3bdQlLCL5kdUI4DMVKQv8pDp16D766CN94pwpRsbIJ8Z04GdOQIUEAUEgbyAg4pd5o56llIKAICAIxEQAPVvM+CPGiMgaYq1uhGgdooR+RYDd4sqEZ4ikYxET0dWtW7dqMWL2OzGYgiEbxEedPtgyId+Sh9QhgLg/YpeIQhu+57pgwQLdZiKpGKQuh/+bEiLeiPG3bNlSFnSpBl/SEwTSjIAs6tJcAZmaPJbvbrnlljC/UuxOuv0x0OUmypaySz7Tx3X4nHNrC85njRs3DtOpTF+OvaXMBPWZZ57RunH/5//8H1d9N/ju/vvv10ZUsPwHFolSuvFEXwoXJ5SnTp062lw9lkgfe+wxrY/MSR1GldKdz0Rxdn4vfYgTkci/r7nmGvX++++rGTNmaOuptHWsqA4ePFh16NBB3XzzzdrIWLrHTfKEvl8mWW2NjKq8EQQEgSAREPHLINGUuAQBQUAQEAQEAUFAEBAEBAFBQBBIMQJyUpdiwCU5QUAQEAQEAUFAEBAEBAFBQBAQBIJEQBZ1QaIpcQkCgoAgIAgIAoKAICAICAKCgCCQYgRkUZdiwCU5QUAQEAQEAUFAEBAEBAFBQBAQBIJEQBZ1QaIpcQkCgoAgIAgIAoKAICAICAKCgCCQYgRkURcg4Pgzql69unr88cfV77//HmDMEpUgIAgIAoKAICAICAKCQCQEZA4WCRl5nlcQkEVdEmp67Nix6ueff05CzBKlICAICAKCgCAgCAgCgkAkBGQOFgkZeZ7bETghtxcwleUrUKCAeu+997TDz1NOOcVX0ocOHVJLlizRfm7OOeccVblyZV/fpzPwX3/9pZYtW6YWLlyo7rvvPnX77bdHzY4pK999//336vTTT1cvvvhi1G+CeGnSnTdvnqd8BpFmXoxj06ZNCge4ixYtUvhpg5+TQaQD361cuVK1aNEi1zraTRWeidZRtuQz0XKm6nvBM1VI5910zJiY6rE4WYgnMgdLVp4kXkEglQjISV3AaG/btk1ddNFF6oYbbvAV8+HDh9X8+fPVm2++qehgs4n++9//qunTp6v+/furgwcPxsw6ZV26dKn64Ycf9B/fp4IMxsOHD/eUz1TkKTem8dtvv6nRo0erWbNmqWPHjiWtiKTz2Wefqc2bNyctjaAj/ueffxSOrO+55x7Pp/mpwjPRsgaZT/qHm266SfXt21cdPXo00ayl/fvcXO9Bgpvb6j1IbJIRV7rG4kTKgoglm+d///23azTxzsFcI5OHgkCWIfA/nTp16pRlec7Y7K5bt0795z//Uc8//7w688wzfeXzpJNOUnfffbeiw+Jk4+abb/b1vT0wCysWLtdff7068cQT7a+Scn/GGWfoCdi3336r7r//fnXZZZdFTYey3nHHHXpiy4Ttzz//1N9F/SiAl6RbtGhRtWrVKn2Nlc8AksyTUVxyySXquuuu05sUFSpUUKeddlpScCCd8847Ty1fvlyVL18+JbyeaEGOP/54BR9ec8016q677vKU51ThmWjZgswnGJ199tn6RP3CCy+MmLVU93URMxLjRW6u9xhF9/U6t9W7r8J7DBwkz4N3OsZij0UNBaPMnTt3Vq+99ppq3769yp8/v2ufn8gcLJSY3AgCWYyAiF8GWHmFCxfWu/ABRhlXVPv371fsmgsJAoJAZiFw3HHHqXvvvVf/ZVbOMis3TNrq168fM1PZ0tdJvcesSh0gt9W7t1L7C5UtPO+vVNFDn3zyyapVq1Z6w3zgwIGK0zg3ypQ5mFve5JkgkAoERPwyFSinMA1EGd9++23166+/pjBVScorApxKDhkyJIfoCIZ1pk2bFooGca2tW7eGfstN7kFg7969auPGjbmnQEkoyZEjR9SWLVuiiqIH2ddZlqWlJJIpCi71HptRcmO9xy619xBB8rz3VNMfkk2Rs846S6Ezd9VVV6U/Q5IDQSBDEZBFXRorZvfu3erVV19Vl19+uRa3HDNmjGIy7yREMnv06KFF2ujcEG176623whYGyJdjlAJRSMQUkDlH7I3wiKbt2rXLGa366aefVJ06dbSoKOKegwYNCoszxwceH6Ar9+ijj+p4if+XX37x+GXOYEy20NcrW7asLgsiquhroeDtJJ7xjjCUG6MtfEsckQhcwIfw/I0aNSpSUNfn7JqCG9hjgAXDLw899JAuO64twNhQ165dtdgIBkQMmXpDHG/Hjh368Zw5cxQ7k8SLThE6moidYBAEXOGXTz/91ETh6+qFlygDeYU3cc3xxhtv6DThO/iKiZed7HWE2PHTTz+dsJ6bPU7qhbRbt26tF8RuOlbUo2lL4BMrn8TpxktffvllqOzwbcOGDXVdwktffPFF3DqChs8Q+b3yyivViBEj7BCG3dvLHhSe2VLvtD/KzI477chtR960maD6OuJ79tln1YMPPqimTp2q+5o777xT6/vCR/DTU089pRd9YRXl4Uc21DuYm/7vlltu0ca6KJr9OX2SIdo/YxVjBt/RN2NtkHZfrly5uKREclu9g1Ws8cjP2BEPz5v6CurqpQ8xabEhAz/Qlmk/9M0ffPCB5hmuQoKAIJAcBGRRlxxcY8bKjluHDh30ztOaNWu0/hEfTZkyJexbOtImTZqoc889V1vHZMLH4mHFihXq448/DoU99dRT9SKA97Nnz1aNGjVSBw4c0Auar776Sn8fCqyUIk0Wf88995zas2eP/oZBhjy5LZjs30a7ZxFF3pi0smgtU6aMatOmTVwTItL55JNPVK9evfTCAqMb48ePVzNmzFD9+vULW1yQ5549e+p3hCHsgAEDdHmwkBiJwJW4XnrpJZ3fWrVqRQrq+hydRSagnLxMnjxZ1yMTFOrt1ltvVYiKmIU6ugDoOtqJeuNklQWLoZIlS6ouXbrosjBJWrBgga4fMOV74vz66699L8C98lLBggX1hgAWLDHc88ADD+iFOQtJFs1OPNGlZJOBP3iJAfz//t//q/nPlMnv9bvvvtP6qZQZngaDfPnyqcWLF4fwNHHCZyyAH3nkEZ1PJubklc0FO3nhJRZcTKAw2ADObdu2VZywDBs2TA0ePFhPZsmPX4LPaIcYdQHTaBQ0ntlU77S/ffv26c0R2pUbBd3XER/WU0844QS9CcPCvl69eqp58+ZaLxleoM5pD34pG+odzGlXpUqV0vxtcK9Zs6Z6/fXX1cyZMxV9kiHaFn0wYxX9bPfu3XVbeeyxx1SDBg30RN6E9XrNbfXuZTzyM3b45XmvuHsN57UPIT7CMuZXqVJFj6nosNO22JTs06ePYuNASBAQBJKDgCzqkoNrzFgZELFyyeSBDpu/J554QmFYwk5McFicYS2PMBCGVBiA3Xax7d9GumdBycQSgy4oSaPEz44aA/LOnTt15xvp21jPUWhmgMZNAR05u+2crDAx8kuIHzIRZ7F222236V1hFhwdO3ZUTPrtcbKQZWLCYogw7CCjnwFFs47ISdSECRO08nU8pvc5UePEh93Ia6+9NmQkh+cMapTBbs30f/7nf3LAAE6cOjiJHXCsqFLv4AmuLGxYeFBffskrLxUqVEiXCb5jYmt45MYbb9R8t3bt2lDSiJOyKGXjgbzCS+SvZcuWyq9bj1CkSum6rFq1qq5LnlNeFr5gDI/ZiXw2a9YslE9OPZmE2k9JvfISxoUqVqyoo6cMlAVe4soCj40UTl8SITceMPElA89sqneDA3zEX6Lkp6+j/eNKhrYLv9HnwkcYbDELnUTyk6n1TpkoHzxuF/mmXSHuRv9miA0PFnn0RaafLVasmO7n2dChzdJXxUu5pd69jEd+x454MQ3iO699CGmx+QEPsKHL2MY98xw23xjHY7k8CiK/EocgkFcRSHzUzKvIJVBuTm7o9EuUKBE2OWfy6Bz4kR9n55jJJsRpGqds9gmr36ygb8dCwymbzkSGCX0ijtM5WaIjN8SECDEdu8iheRfryiLtggsu0JYC7WERR2SiwcIOYtH4zTff6AWH3VIe1vg4PXMulE1c7CiyIKldu7ZeLJnn8V6vuOIKvQAw31OfiUxM4QXigLBSxiQgEfLLS+CHHoMhN/5ERBFewvy8nRItO4s3RLzsBn8oP4tHe55I00s+vfKSKYMzTp6b+o13M8XEHe2aDDyzqd6jYRPPOz99HW3VtDfSYjPF2R/Hkwcv36S73pl4Mx7Rjxrx5vXr12vRby/9TrIs3HrBzi1MOus9nvHI9C2mLIn2nyaeoK5++xBnupxK2ucFzvfyWxAQBIJBQBZ1weDoKxY6fXZB2en0Quw2IwrGQga9DxYiDLjxEqdxTJgxB8/gYf6Y1CCS6dSZ8pOOcxJE3DyLJ07E/C6++OLQCaXJB3EyCGK+mJ1jFsnsAjoXqYTjZMttMGHnEbFLFteJYGnylC3XoHmJUzMwTmR33g07Ngc4jS5durTW2eGEjDpjghnPQtkrL7nlxTwjbfw62U9ezbugrsnCM1vqPSgcTTzJ7OtMGkFcM6HeOZnEPQiSDYibLly4MMdmDQtd2iUi5my4GLFUTu840c8USme9xzMeZQpu0fLhtQ9hQ46wbBAw7jNGo4989dVX61PvaGnIO0FAEEgMAVnUJYZfQl9zihWLEA9ESX/79u1aTBB9unfeeUcb94j1bbT3TJoZ+BiUnX+I1mQjecHTlOvDDz/UopLouaF7x6ldbqdk8RK4B+0PkYV4jRo1tB4pOnqIK3OSgF4a/JqbKWg8s6nek1Gv2dLXpbveMeLDCTV60Yw3iBm7bTyy0cLiDnUBNlg4PUd0n8l8JlG6693PeJRJuLnlxU8fwmYxqgfoXyNpg5QRhlPQ83du+rqlJc8EAUEgfgRkURc/dnF/aU6v6Ohi0cSJE7X4IYuP888/P1ZwT+85WYEQ5Uw2cSrJTp2bzlistJkkoGNkDI3Yw2MpElFRIyKF+E8kM/GU03lSiA4WAw5WDTnhwxCGETuypxP0vdupYdBpRIovGbzEII2oE3WcDDInAxiI4XQZYyXRdCQj5cErL0X6nufwIoTxi2RRMvDMxnoPCt9U9nWJ5DkT6p22hg9FpBc4ZcHQE2LfdqKPxHohlg0x6MMGC0abMKbEuJYplM56py79jkeZglukfPjpQ1avXq3Qu8bIFRsDjMtYbzY67pHSkOeCgCCQOAKyqEscQ98xYEAChWFEXeyLDSaNS5YsCYsP/R0WRJEWA1gfi2WtkveTJk0KxYtRD3YR0TNy0ty5c0MmrZ3vvPxGrNROiMyhL2K3nmZ/H+0es+IbNmwIU94nPDjNnz9fK2Lzm4kHokNMREjPToiBYA3TuWBj4IXAlV1mJiaY8k82sRNOXuynTeQ5ET1Gr3kOgpecabGwhp+dCy0snplFkPObWL9ZxMObdoz4BkMsWE7jhNkveeUlEy98bG+bPIfnOLm49NJLTbDAr8nAM1vqPQgwU9nXBZFfE0em1DsilFgvRrzSqSdr8oqoKKLriERjrIp2zjiUTsqkeo9nPEoEO2fZE4kr0rd++xBOe3v37q3H5GRu+kXKr5fnuOHgdJrNQudY4+V7CSMIZCICsqhLU61UqlRJbdq0SY0bN05PHll8MEiyw4dzaiwyQlgkwym1MafNAIpYw+eff65PR9AHcy4EWThwksU7Blt2Xu0nZRhEwS8T5oUx/87klT/uMQPv1E3zChGTe8qAERc6SRYr/fv31xbUihQp4hoNCxy3kzQCs/isVq2aatWqVShO9Di6deumd5FZGBtCJIi48OtmFhNcmXwg/mGU/ckX4eynf6RTv3597ToBMRO/BHboWhGvnUiLEyz7KRaiKRi6YfFM3YARp4ToabGzSb2bPBIf94Qz98TPgoOy2ctgTzfSvR9eIm57msRp8kWeuYcwTEMdgTODN8/RxcEwDjzIwI4olx8iXU7mmFjiTgACA9wasOtrX1R5zacfXiI9NlzQY6WspE0bfPfdd1XdunUT1h+kfJF4Phl4Zku923kEzFk8ODeJ7GG4D6qvM7xt+Jo6MvxP+6a+4IVEKJPr3ZSLSS4LOyxaukmGsBmGWDQLF3zXYbq+ePHiuo/v1KmT1qUyccVzzS317nU88jN2GDxj8bwJ5/cajT/99CHoziF+aQyR4RrDSMUkunHKApZNYk4DvUg6xcKAeRf6oPQ1QoJArkDAEkobAps3b7bq1atn5cuXz6pSpYq1ZMkSq0+fPla/fv2s1atX63wdPnzYGjp0qFWkSBFm0ValSpWsmTNnWhs2bLBKlSplVatWzdq+fXtYGY4dO2aNHz9ef1OoUCFr0KBBFvE4afny5Vbt2rV1+oQj3QMHDjiDefq9c+dOnc/du3dbHTt2tIivcOHCVv/+/XPEuXbtWqto0aK6PJTJ/jd79uyw9I4ePWpNmzbNKlOmjA531113WaNHj3Ytz65du6zu3bvrdA1W8+bNs8ADIo/lypULpTdy5Ej93J4f8sVvrxQpTsphymWP01431Dv4//jjjxr7YcOG6XonX+Zb4unSpYv+Td7XrVsXKgPP/ZBXXrKnT5qUEb5o1KhRKF/2tImXOoFHDR8tW7bMatasmTV58mRr3759frKp6wu+gY8eeeQRzZ/58+e3GjRoYNFmDPnNp1deAnPKOmfOHN0uqQva5+LFi0O8ZPLg9erkE1O/dt4wcQWNZ7bUO+W316nBiKud3wxOXO3tKd6+zl431Dv9sOmfyI/Jk1td2fPidm+P214et7jSVe/OfH/88ceWsx82YeirOnToYB08eNA80tf9+/dbbdu2tcaMGRP23OsPg7Edo2yud8od73gUaewwWHrleRM+2tU+9jmxt/OA1z7kyJEjVt++fa358+eHJUvfy7Pq1atbzBH8knP8MXl1a0d+4p40aZLVokUL69ChQ34+k7CCQMYicBw5yxWrUymEICAICAIBIMAJBKem+HJEz0hIEBAE/hcB/IYiaYCvUKcO3UcffaQNYWSroS2p48QRQILkueeeUxi3uu6668Ii5OSuXbt22ll9MvWSwxKN8oNTUiR7UA2JRz0kStTyShBIGwIifpk26CVhQUAQyEQEELNDnJZBX0gQEAT+RQCRdzY9MJxhxL+5IhqN2fpIYvb/xiB3uRkBVDuKFi2qLUrjyJ4zA/4wmIJIPSK9Tj+j6cCD/h01F3StsaosJAjkFgRkUZdbajLgcuADDqMU7MbG+mOQz02ULWWXfAbPdV27dtXO6ocOHaotAYKxGxEuVrvgfePGjcN0Kt3i8vtM6t0vYsGFz+v1fs0116j3339fzZgxQ7swgMexLItV2g4dOqibb75ZG9rKbWNHOuvdD/emO5/oXD7//PPaGmqdOnW0ywvcXjz22GNar5+TOoyTpTufuOChb8bnIjwsJAjkFgRE/DK31KSUQxAQBAQBQUAQEAQEAUFAEBAE8iQCclKXJ6tdCi0ICAKCgCAgCAgCgoAgIAgIArkFAVnU5ZaalHIIAoKAICAICAKCgCAgCAgCgkCeREAWdXmy2qXQgoAgIAgIAoKAICAICAKCgCCQWxCQRV1uqUkphyAgCAgCgoAgIAgIAoKAICAI5EkEZFEXYLXjh6V69erq8ccfV7///nuAMUtUgoAgIAgIAoKAICAI5D0Exo4dqy677DI1ZswY7SIh7yEgJRYEvCEgizpvOPkKRQf0888/+/pGAgsCgoAgIAgIAoKAICAI5ETgv//9r5o5c6Y6ePBgzpfyRBAQBDQCJwgOwSFQoEAB9d5776lTTz1VnXLKKb4iPnTokFqyZIn28XPOOeeoypUr+/o+nYH/+usvtWzZMrVw4UJ13333qdtvvz1qdkxZ+e77779Xp59+unrxxRejfhPES5PuvHnzPOUziDTzYhybNm1SP/74o1q0aJH2BQQ/J4NIB75buXKlatGihW53yUgn3XGmCs9Ey5kt+Uy0nPK9IJDpCNAWN2zYoLZt26a+++47Pb7iYzBbqVq1atrP3bRp0xS+8IQEAUHAHQE5qXPHJe6ndKIXXXSRuuGGG3zFcfjwYTV//nz15ptvKhY72UTsoE2fPl3179/f0y4aZV26dKn64Ycf9B/fp4IMxsOHD/eUz1TkKTem8dtvv6nRo0erWbNmqWPHjiWtiKTz2Wefqc2bNyctjaAj/ueffxQOeO+55x7Pp/mpwjPRsmZLPhMtZ7zfI57Ppt/ff/8dbxRZ+V08PJ+VBc2gTNMWmU+w4bVixYqk9sNBFJtx4ssvv9Qbw27xHTlyRJcDJ+YnnXSSWxB5JggIAkopOakLkA3WrVunxo0bp1q2bOn71OCMM85QL7zwgtq3b1/COUI8YdSoUapmzZq+8xFP4hdeeKFq2rSpmjt3rqfPKWvjxo11WPL5yy+/ePou0UCk27BhQ7VmzZpEo5LvoyBw9913K07nOnXqFCVU4q9I5+jRo2rEiBGJR5aiGE488URVvnx5df3116uLL77YU6qpwtNTZqIEypZ8RilC4K/oi19//XU1Z84cfarMiUPt2rUDTyeTI/TD80huWJalihcvnslFSkregiw7bZG/Xbt26RO7pGQ4gEgXL16sXnnlFcWGB5JKs2fPzhErm7GDBw9Wd9xxhypRokSO9/JAEBAE/kVAFnX/YpHwXeHChfUufMIRJRjB/v37FTt1QoKAIJBZCBx33HHq3nvv1X+ZlTPJTTIQOPnkk1WrVq3U888/rwYOHKjF4ZKRTibH6Yfn169fry6//PJMLk7S8pYXy16kSBH18ccfq40bN6r69eu7YsumAJvGQoKAIBAbARG/jI1RVoVAlPHtt99Wv/76a1blO69k9s8//1RDhgzJIYKFYR30BQwhsrR161bzU665CIG9e/fqSUwuKlLGFiXd7YgFzVlnnaXQt77qqqsyFqdkZywWz3M6h072gAEDAslKuuvdTyGCLruftNMdFlHKc889Vy/kkfgREgQEgcQQkEVdYvgl9PXu3bvVq6++qju0m2++WZvrZTByEqIJPXr0UNddd51iksD1rbfeClsYoKeBSCMihp07d9a6G6eddpoOj7gXYhhO+umnn1SdOnXUmWeeqUh/0KBBYXE6w3v9ja7co48+quMl/kTEKxnw0NcrW7asLgsiJehrYfTESTzjHWHACaMtfEsckQhcwIfw/CEO6oc4FQU3sMcACyI0Dz30kC47ri3A2FDXrl1V/vz5tQER88zUG0rsO3bs0I8R1WKHn3j79u2rdTRfe+01rR8Bruxkf/rppyYKX1cvvEQZyCu8iWuON954Q6cJ36EThH6Dnex1BC89/fTTCeu52eM0PN+6dWu9IEbk0knUo2lL4BMrn8TpxkvodZiyw7eI61ImeOmLL76IWzfF8Blmua+88sqoIqP2sgeFZ6bXu592RN2jgzNjxgxVpUoVXT/gRHtDV9dQPO2IPmTo0KG6PyRO+i90kjKJvJTd8Jvp1+BpyP6cPsuu37dlyxbddik3bYj29MEHH2gsuPolk1Ysnqeejj/+eHXnnXdqPTD0TU2+3frjWHUUT737KRunSvQLLEb4Q0eWsdyQn/7Tb9lNGkFdvfCSSYt+H5cCzBWoH8ZkLH3T35crV06kgwxQchUE0omAJZQWBPbv3281bdrU6tevn3XgwAH9N3r0aKtYsWLWyJEjQ3nasWOHVbVqVWvw4ME6DC92795tPfPMM9aQIUNC4ew3s2fPtho1ahQKb39n7levXm3VqlXLWrhwoXX06FFr7969Vq9evaxWrVpZBw8eNME8X3fu3GmVK1fO6tKlizVs2DCL8h0+fNgaPny4zj/lcCPKyjeRCEzKly9vLV682Dp27Ji1bds2q0GDBlaPHj10/OY78ty5c2f9jjCEXbp0qVWyZElryZIlJpjGBGzAyNCaNWusl156SeNqnnm9ku706dN12du3b6/rEyx53q1bN6tFixbWoUOHQtGBh7NuwKl169Zh9Q4m5H3lypXWli1bdPyE2bdvnzVp0qQccYQSiHLjlZc2btxo9e7d26pWrZrVpk2bEI+sWLHCuv/++61FixaFpTJz5kzr4YcftlatWqV5af369Zo/K1eubMEX8dCsWbOsJk2a6PrmezAFk8aNG4fxNfVIPtu1axfK57p166yyZcta8+fPD0vaCy/RLl5++WWrYsWKui1QFniJa6VKlaxRo0bp32ER+/wBD0Tj+aDxzIZ699uOqEvq48cff9T1QR82ZcoUXW+0f0N+2hHtsGfPnrofhN+Ik/4R3uaaKNHXOdt+PHF6LTuYwssdO3YM41n6O/oS+mhD8EiNGjWsCRMm6H6Vd3zXvHlza+rUqTnavPnO6zUWzxMP46Czb3bG77WO/NS7M41ov5ctW2aVKFEihBN5ZtwBT9PP++0/vZY9Wr4ivaP/rV69urV27VrXIF55iY8J27BhQ90n0ycuWLDAKl26tDVx4kRr7NixYfzkmliUh2b+YB+XowSXV4KAIBABATmpS9OKesqUKdrKZb169bQxE9wgPPHEE6pChQphOcJwyoEDB7S1PMJAGKEoVapU3PoZiGhiZRM9D5SP2SVld7ZBgwZq586d+kQoLBM+fmAYoFatWtpNwQknnKBPrThZse+ge40O8cNPPvlE9ezZU9122216d7BgwYKqY8eO2kyzPU4UrFG67tKliyIMO4mcikHRrCNyEjVhwgTVvn17javXvJlwnKhx4sPO9rXXXqsxBUuec4pAGezWTN3MMYMTJ6xOYicUK6rUO24fwDVfvnz6pIc0/JJXXipUqJAuE3zXvHnzEI/ceOONmu/Wrl0bShpxUsR9mzRpovMKL3EShbEgv249QpEqpeuyatWqui55Tnk5qQNjp58i8tmsWbNQPjn1LFmyZNgpqVdewoBJxYoVdVYoA2WBl7i2bdtW639wApEIufGAiS8ZeGZDvfttR7RpXKeY+oHv+E37cBqb8tqOsNaKLvJzzz2n+Y046R8bNWqk692uw4JaAAAgAElEQVRNisLUWyqvXssOpmXKlFGcwNFGDG3fvl3RtuhTDOGChN+Epz/inrGJEyj63lhuakw8ka7ReD7SN27P/dSR13p3S8ftmRk3n332WVWpUiWNE30z4wyn+ubU00//6ZZOKp955SXKho84xiAzvhYrVkyP73v27MnBT6ksg6QlCAgC/yIghlL+xSJld0wOWIRgyck+OWfy6Bz80MNAJMwQYkoM0oj1OcOaMLGu6Nux0HDqeJAXBiT0u+IdxBHDYFJg6Oyzz9biGvgse/DBB81jT1cWaRdccIFy+tfBZQQLKfzvMOli0fjNN9/oBYddLv+SSy5RkydPjmgBFJE0FiQoYTMZTJSuuOIKvQAw8VCfTAzjJeqXOCB0D5ikJUJ+eQn80Acy5MafTGbgpZtuuskE09dEy87iDVEfRD6ZRECUn8UjivN28pJPr7xk4nXGyXNTv7gtOe+880zQQK/JwDOb6t2OswHWjZdY4BtCLIyFNtbz7Bso5r2XdoTIK+LT9CfOdka9IxrNpD4TzKn7KTubQizMMMKB2Bx9JVaaEVWNRbQze18eK3yy3/utIy/17ifPYMiiH0NHpl/me0Qx69atm2NjztmHuPWfftJPRlg/vOSWPioeQoKAIJA5CMQ/48ycMmRdThhY2Tk1k9VYBWAygeU0FjIsjFiIMMDES5zGMWFmYspAY/5YgKCP59SZ8pOOc6FpBrJ44sSxNGbfzQmlyQdxMtFicsIOIotkJi7ORSrhWKy5TUzYze/Xr59eXCeCpclTtlyD5iVOzcDYvusfBBZsDnAaXbp0aa27gYU06owJdzwLZa+8FC3vpI15bbeFQ7Tv/LxLFp7ZUu9esWKCT52iz8OGAhPrb7/9NuxEymtchAN3NruefPLJUH9o+kVOb+kz0T/KBPJTdja52KD7+uuvtW4xJzMs1pD2sBObKPAIm2P01fSr6JBeffXVio25TKB01xHtnr7OiQcLfTdJi0zALFYevPISYzD9MTqOLGz5jtNdTu/YCBESBASBzEBAFnVprAfn4OCWFcQDn3rqKYXIDGKC7Ca/88472riHW3ivz5g0M1Ghc3b+IWKRjeQFT1OuDz/8UItKInaJxTVO7XI7JYuXwN15epYolizEa9SooU9fMNiAuDIn21999ZXm10Tjz+Tvg8Yzm+rdS73QX2EQCTFJfL7hYPnzzz/XIr+c7CdCI0eOzNEfkh58h1GMdJPfsrMwZWOE9sMYwsK3aNGiOYrBBh/i4uAKhixkkQhB9NS5UZfj4xQ/SGcdBd02UwxdWHJ+eQk+YnGHmggba0hNoLLBhoCQICAIZAYC/8rJZUZ+8kQuGGgZKBk0Y9HEiRO1+CGLD7cTp1jfu703ooaIciZ7osKpJLu+mPT2SwwW8+fP1ydxTrEnxKEQFWWQ4aQOMRCskmGl0EmUE/0uO37oYDF5ofzsTg8bNky9+OKLSZ/A2PPgzGeyfyeDl+BjTjio42SI4pgdYnaJmbx36NBBTzipez/klZeixYm+G5TMNpMMPLOx3qPVA4sTrO71799f3XrrrdGCen5H/4JulLFA6/nDFAeMp+z4T2XTBZF/TuqwoOuk1atXK3RlR4wYkfQ+0Jm219/priPGEE6pONF09nWcbnKSmE0ndn54iXEcC6icjCPhIiQICAKZiYCc1KWhXhgcUD5fvnx5mKgjk0b0QuyE/g4DRaTFACJBbub97XHwftKkSaFHGPVgxxE9IyfNnTtXizU6n3v9bVfI5xtE5tATwnCFX8LE9YYNG3L4awMnFnso9UMM9pjBZnHmNJLAADx+/HitS2JP3+w+gyu7jYgnYYo62YSeBQMku6SGyDN6jMmmIHjJmUcWV/Azk0U7rVq1SplFkP25l3sW6fCmHSO+Qz/olltu0SfMXuKxh/HKS+Yb+NgpMgzPITJ96aWXmmCBX5OBZ7bUu1cwaS/Uj3Nibf+ePs+PuCT9AX0U/Z+Tb+lDWETSbtNN8ZSdTQhE99nIov8xm3rOsuD+pXfv3rofNRs1zjCp/s1GDuMHlO46wigPm0zOMZq80TfY3dcEgZO97EHE54zDLy+xaGVBhyg8RspoJ37amDP9ZPxG4qZ69epaZxQJBSFBIK8hIIu6NNU41rM2bdqkxo0bpyePTBzoLJmo4JwaUUuIgQSn1MivQ3SkiMggbsTpCPpgzkGGgZsdZ97R6bJDa99BxCAKFrz69Omjpk6dqtNnAss9Puacumk6YQ//mNxTBgY3JuQMGuymc0pXpEgR1xiYKHGS5pxAE5jFZ7Vq1VSrVq1CcbJT2q1bN71Dz8LYEKIhxIVfNzMp48oghCiRMX5AvgjHwsEQ6dSvX1/16tVL+2Uzz71eyTu6Vs5JH2lRR/wZQsxpzZo1evJI3YARp4Toa7BLTr2bPBIf94Qz98TDhJay2ctg4o929cNLxG1Pk3hNvsgz9xA6O9QRODMR5DlOhDGMAw8ySWRH2A+R7vDhw7X+Bk6LITBYsGCBPo21L6q85tMPL5EeGy7osVJW0qYNvvvuu9ogQqL6g5QvEs8nA89sqXev7Qh9MPoaRCJZvPEdvuRo+0g/8AxxQ/pUsDZ8HKsdcRp8/vnn6/6FfgbiiqVg9PbMRpB+4fMfeWJxwomYFwmNSNH7KbuJw4hgIpnA5oYboTuH+KUxHlWzZk0tyYDkQxCbXdF43uSHBROnioiIghd9HPd2kVovdWT6Ka/1btKPdQV7xH2xsIwaBHzHH/csiNl0MuS1XzLhvZTdhPVzhedZjDk3W4nDDy/B+4jDs4GKb702bdqo4sWL67G9U6dOuq35yZczLBtPjBOMG9R9osQmTCo2ShPNp3wvCASOQARXB/I4BQhs3rzZqlevnpUvXz6rSpUq2p9anz59tK8z/GVB+OUZOnSoVaRIEWbR2jcTfqw2bNhglSpVSvvo2r59e1hu8SEzfvx4/U2hQoWsQYMGhfl0M4GXL19u1a5dW6dPOOMzz7z3c8XPDPnEhx7+jYivcOHCVv/+/cP8ihEnPnOKFi2qy0OZ7H9OPzX4ipo2bZpVpkwZHe6uu+7S/nLAxUm7du2yunfvrtM1WM2bNy/ko8n4wjHpGX+A9vyQr0g+fZzp8TtSnJTDpGOP01431Dv442sL7PHvR72TL/Mt8eBzid/4AcQHG1d+R/N15pZXr7xkT5+0KKPxIWXyZU+bePFhBI8aPsKfU7NmzazJkydr33pu+Yn0DIzgG/jokUce0fyZP39+7YOQNmPIbz698hKY4y9rzpw5ul1SZtqn8ZVo0vdzdfKJwdHOGya+oPHMhnp34mPaZqR2hN9Ae9/1+uuvaz4dMGCAVbBgQc2Pdv7w2o7g84EDB4b6W9Mvm7rxe3W2m2j17jVuL2WnDdnpjz/+sLp27ZqjLybMkSNHrL59++bw60h7wdcjfs7o1/2Ss069lB1/efghNWMifaOTYtVRPPXuTCPSb3Bl/DX9Ev0dvg3xH2rInr6X/tN856XsJmysqxkzDObm6vST6JWXqIcOHTrk8GGLP8O2bdtaY8aMiZUl1/f29m3yyNU+vrh+GOXhnj17rLp16ybsWzFKEvJKEMhYBI4jZ4GvFCVCQUAQEASyFAF2ojk15YSGHXQhQSA3I8CJGEZnMEiECxE7cXLXrl071b1796TqktrTlPvMQwB/sUiY4COWk187ffTRR/oUO1MMrJFPjOnAz9J/22tK7vMCAiJ+mRdqWcooCAgCnhFA5BJxWkSrhASB3I4A4vhYxMQK8NatW7UINXu9+P5DDBqRVLu/ytyOh5QvJwKoOrDZhdElI/bPFZF4XF9EUq/IGVNyn6CmggpDy5YtZUGXXKgl9gxFQBZ1GVox6c4WPuAwSsGuXKw/OvvcRNlSdsln8FzXtWtXVaFCBTV06FDtZBiM3YhwsdoF7xs3bhymU+kWl99nUu9+EQsufDLqPRlx+ikx+lLPP/+8euCBB1SdOnW0uXpM1j/22GNaF5uTOgxKpTuffsrkJay0Iy8o/W+Ya665Rr3//vtqxowZ2oUBfRsWhQcPHqwtEuPYPhPwJE/o+2HJVkgQyIsIiPhlXqx1KbMgIAgIAoKAICAICAKCgCAgCOQaBOSkLtdUpRREEBAEBAFBQBAQBAQBQUAQEATyIgKyqMuLtS5lFgQEAUFAEBAEBAFBQBAQBASBXIOALOpyTVVKQQQBQUAQEAQEAUFAEBAEBAFBIC8iIIu6vFjrUmZBQBAQBAQBQUAQEAQEAUFAEMg1CMiiLsCqxKdP9erV1eOPP65+//33AGOWqAQBQUAQEAQEAUFAEMh7CIwdO1ZddtllasyYMdrlRt5DQEosCHhDQBZ13nDyFYoO6Oeff/b1jQQWBAQBQUAQEAQEAUFAEMiJwH//+181c+ZMdfDgwZwv5YkgIAhoBE4QHIJDoECBAuq9997TTi9POeUUXxEfOnRILVmyRPt6Oeecc1TlypV9fZ/OwH/99ZdatmyZWrhwobrvvvvU7bffHjU7pqx89/3336vTTz9dvfjii1G/CeKlSXfevHme8hlEmnkxjk2bNimcwC5atEj7aYOfk0GkA9+tXLlStWjRItc6m00VnonWUbbkM9FyyveCQKYjQFvcsGGD2rZtm/ruu+/0+IqvuWylatWqaZ+J06ZNU/hVFBIEBAF3BOSkzh2XuJ/SiV500UXqhhtu8BXH4cOH1fz589Wbb76pWOxkE7GDNn36dNW/f39Pu2iUdenSpeqHH37Qf3yfCjIYDx8+3FM+U5Gn3JjGb7/9pkaPHq1mzZqljh07lrQiks5nn32mNm/enLQ0go74n3/+UThzvueeezyf5qcKz0TLmi35TLScfr/fuHGjat68uTr33HPVmWeeqapUqaLbhmVZfqPKyvDx8HxWFjSDMk1bZD7BhteKFSuS2g8HUWzGiS+//FJvDLvFd+TIEV2Oxx57TJ100kluQeSZICAIKKXkpC5ANli3bp0aN26catmype9TgzPOOEO98MILat++fQnnCPGEUaNGqZo1a/rORzyJX3jhhapp06Zq7ty5nj6nrI0bN9Zhyecvv/zi6btEA5Fuw4YN1Zo1axKNSr6PgsDdd9+tOJ3r1KlTlFCJvyKdo0ePqhEjRiQeWYpiOPHEE1X58uXV9ddfry6++GJPqaYKT0+ZiRIoW/IZpQiBv1q8eLHq1auX6ty5s+rbt69CWmDkyJGqQYMG6sMPP1QlS5YMPM1Mi9APzyO5wWK3ePHimVaMpOcnyLLTFvnbtWuXPrFLeubjTID28corryjsESCpNHv27BwxsRk7ePBgdccdd6gSJUrkeC8PBAFB4F8EZFH3LxYJ3xUuXFjvwiccUYIR7N+/X7FTJyQICAKZhcBxxx2n7r33Xv2XWTmT3ASNABsO//nPf9RDDz2kGBugU089VdWuXVufoHAywcQ7t4uT+eH59evXq8svvzzoqsiK+PJi2YsUKaI+/vhjxWl2/fr1XeuJTQE2jYUEAUEgNgIifhkbo6wKgSjj22+/rX799desyndeyeyff/6phgwZov7++++wImNYB30BQ4gsbd261fyUay5CYO/evXoSk4uKlLFFSWc7Iu3du3drvWF7ez/55JPVeeedp98RJi9QLJ7ndA6d7AEDBgQCRzrr3W8Bgi673/TTGR5RSsSSWcgj8SMkCAgCiSEgi7rE8Evoawb8V199VXdoN998szbX6zbII5rQo0cPdd111yl2Pbm+9dZbYQsDJg2INCJiiKgPBltOO+00HR5xL8QwnPTTTz+pOnXqaD0P0h80aFBYnM7wXn+jK/foo4/qeIk/EfFKBjz09cqWLavLws42+lqIMTmJZ7wjDDhhtIVviSMSgQv4EJ4/xEH9EKei4Ab2GGBBhIadeXRncG0Bxoa6du2q8ufPrw2ImGem3lBi37Fjh348Z84cxcSPeBHZQkfztdde07v74MoA+Omnn5oofF298BJlIK/wJq453njjDZ0mfAdfod9gJ3sdUe6nn346YT03e5yG51u3bq0XxJyAOIl6NG0JfGLlkzjdeInTE1N2+BZxXcoEL33xxRdx66YYPsMs95VXXhlVZNRe9qDwzPR699OOqHt0cGbMmKH108CIP9oburqG4mlH9CFDhw5V9IfESf+FTlI8xKncU089pU9lOW0wRP3Cw5dcconya1CLOLyU3fCb6dfgacj+nD7LvtjcsmWLbruUmzZEe/rggw80Flz9kkkrFs9TT8cff7y68847tR4Y+qYm3279caw6iqfe/ZSNUyX6BRYj/KEjy1huyE//6bfsJo2grl54yaRFv49LAdoG9cOYjKVv+vty5cqJdJABSq6CQDoRsITSgsD+/futpk2bWv369bMOHDig/0aPHm0VK1bMGjlyZChPO3bssKpWrWoNHjxYh+HF7t27rWeeecYaMmRIKJz9Zvbs2VajRo1C4e3vzP3q1autWrVqWQsXLrSOHj1q7d271+rVq5fVqlUr6+DBgyaY5+vOnTutcuXKWV26dLGGDRtmUb7Dhw9bw4cP1/mnHG5EWfkmEoFJ+fLlrcWLF1vHjh2ztm3bZjVo0MDq0aOHjt98R547d+6s3xGGsEuXLrVKlixpLVmyxATTmIANGBlas2aN9dJLL2lczTOvV9KdPn26Lnv79u11fYIlz7t162a1aNHCOnToUCg68HDWDTi1bt06rN7BhLyvXLnS2rJli46fMPv27bMmTZqUI45QAlFuvPLSxo0brd69e1vVqlWz2rRpE+KRFStWWPfff7+1aNGisFRmzpxpPfzww9aqVas0L61fv17zZ+XKlS34Ih6aNWuW1aRJE13ffA+mYNK4ceMwvqYeyWe7du1C+Vy3bp1VtmxZa/78+WFJe+El2sXLL79sVaxYUbcFygIvca1UqZI1atQo/TssYp8/4IFoPB80ntlQ737bEXVJffz444+6PujDpkyZouuN9m/ITzuiHfbs2VP3g/AbcdI/wttcg6Lff/9d82y8cXotO5jCyx07dgzjWfo7+hL6aEPwSI0aNawJEybofpV3fNe8eXNr6tSpOdq8+c7rNRbPEw/joLNvdsbvtY781LszjWi/ly1bZpUoUSKEE3lm3AFP08/77T+9lj1aviK9o/+tXr26tXbtWtcgXnmJjwnbsGFD3SfTJy5YsMAqXbq0NXHiRGvs2LFh/OSaWJSHZv5gH5ejBJdXgoAgEAEBOalL04p6ypQp2splvXr1tJ4Fu7pPPPGEqlChQliOMJxy4MABbS2PMBBGKEqVKqXNFYcF9vgDEU2sbD7//PNa+ZhdUnZnUd7fuXOnPhHyGFWOYBhpqVWrlnZTcMIJJ+hTK3al7TvoOT6K8ADxw08++UT17NlT3XbbbXp3sGDBgqpjx47aTLM9ThSsUbru0qWLIgw7iZyKQdGsI3ISNWHCBNW+fXuNa4SsRHzMiRonPuxsX3vttRpTsOQ5Vu4og92aqZv+DDhxwuokdkKxokq94/YBXPPly6dPekjDL3nlpUKFCukywXdY7UNBHR658cYbNd+tXbs2lDTipIj7NmnSROeVcJxEYSwonlMIEzF1WbVqVV2XPKO8nNSBsdNPEfls1qxZKJ+cemKAwn5K6pWXMGBSsWJFnQ3KQFngJa5t27bV+h+cQCRCbjxg4ksGntlQ737bEW0a1ymmfuA7ftM+nMamvLYjrLWii/zcc89pfiNOeL9Ro0a63t2kKEy9eb1ySvf555/rvN5yyy1ePwsL57XsYFqmTBnFCRxtxND27dt126JPMYQLEn4Tnv6Ie8YmTqDoe2O5qTHxRLpG4/lI37g991NHXuvdLR23Z2bcfPbZZ1WlSpU0TvTNjDOc6ptTTz/9p1s6qXzmlZcoGz7iGIPM+FqsWDE9vu/ZsycHP6WyDJKWICAI/IuAGEr5F4uU3TE5YBGCJSf75JzJo3Pwu+qqq7SpX5M5xJQYpJmwOsOaMLGu6Nux0CBuO5EXBiT0u+IdxBHDYFJg6Oyzz9biGvgse/DBB81jT1cm9hdccIFy+tdBHJGFFP53mHSxaPzmm2/0gsMul4940+TJkyNaAEUkjQUJSthMBhOlK664Qi8ATDzUJxPDeIn6JQ4I3QMmaYmQX14Cv7POOiuUpBt/MpmBl2666aZQOG4SLTuLN0R9EPlkEgFRfhaPdlE2nnvJp1de0gm5xMlzU7+4LUEnKhmUDDyzqd7tOBt83XiJBb4hxMJYaGM9z76BYt57aUcsthCfpj9xtjPqHdFoJvWJmlOnz2KBxYaBvZ80efVy9VN2NoVYmGGEA7E5+kqsNCOqGotoZ/HmMVbc8bz3W0de6t1PPsCQRT+Gjky/zPeIYtatWzfHxpyXfslP+skI64eX3NJHxUNIEBAEMgeB+GecmVOGrMsJAysDu5msxioAk4mBAwfqhQwLIxYiDDDxEqdxTJiZmDI4mT8WIOjjOXWm/KTjXGgSN8/iiRPH0ph9NyeUJh/EyUSLyQk7iCySmbg4F6mEY7HmNjFhN79fv356cZ0IliZP2XINmpc4NQNj+65/EFiwOcBpdOnSpbXuBhbSqDMm3PEslL3yUrS8kzbmtd0WDtG+8/MuWXhmS717xYoJPnWKPg8bCkysv/3227ATKa9xEQ7c2ex68sknQ/2h6Rc5vaXPRP8oEWJBt3z5cr2J5NYneY3bT9nZ5GKD7uuvv9a6xZzMsFhD2sNObKLAI2yO0VfTr6JDevXVVys25jKBUlFH0cpJu6evc+LBQt9N0iJaXJnyzisvMQbTH6PjyMKW7zjd5fSOjRAhQUAQyAwEZFGXxnpwDg5uWUE8EGV7RGYQE2Q3+Z133tHGPdzCe33GpJmJCp2z8w8Ri2wkL3iacuEjCvFTxC6xuMapXW6nZPESuDtPzxLFkklvjRo19OkLBhsQV+Zk+6uvvtL8mmj8mfx90HhmU717qRf6KwwiISZp3AMg0sjpFyf7iRA+5Jz9Ib/hO4xixEssODEghXGSRBd0fsrOwpSNEdoPYwj5KFq0aI5isMGHuDhxgyELWSRCED11btTl+DjFD5JVR16KEXTb9JJmssL4bUfwEYs71ETYWENqApUNNgSEBAFBIDMQkEVdGuqBgZaBkkEzFk2cOFGLH7L4OP/882MF9/TeiBoiypls4lSSXd94djIZLNAxctNlQRwKUVEGGbBEDASrZG5EOZ0nhehgMXnBqiEnfMOGDdOiSW7fB/kskQldovlIBi+BPScc1HEyyOwQDx8+XJ8u44SW0wa/5JWXosULL0KJTO6jxc+7ZOCZjfUeDScWJ1jd69+/vz49CKJNcdqCbpSxQBstfb/vWEghGoqYtz2vLLbdrPhGiz+esuMjj00XRP5pO26T8NWrVyt0ZUeMGKFFWelLsbhr9JKj5SlV75JZR17KgI4wp1ScaDqJ8SUV46kz3UR+++ElxnEsoHIyDj+zIOT094EHHggTRU0kP/KtICAIJI6ALOoSx9B3DAwOKJ8jimNfbDBpZPC3E/o7LIjskwH7e0SCYk0MeD9p0qTQZxj1YMcRPSMnzZ07V4s1Op97/W1XyOcbRObQE8JwhV/CxPWGDRty+GsDp/nz52ulfuJksMcMNqJDTiMJDMDjx4/PsWBj8gyBK7uNDFCYok42oWfBAMmgaIg8o8eYbAqCl5x5ZGENPzsXWqtWrdILcmd4L79ZxMObdoz4Dv0gjEtwwuyXvPKSiRc+trdNnsNziExfeumlJljg12TgmS317hVM2gv1E02fhz7Pj7gk/QF9FP2fWbyb/NCHsIik3fqlNWvW6FOyZ555JqwPJy78UrptWEVLI56yswmBDjIbWfQ/ZlPPmQ7uX3r37q370WRu1DjTjfZ75cqVIZc4yaqjaOnb32GUh00m5xhNGPoGu2Em+3fx3tvLHm8c0b7zy0uIv6KygCg8RspoJ37aWLS8BPUOiZvq1atrnVE2TYQEgbyGgCzq0lTjWM/atGmTGjdunJ48MnGgs2SignNqRC0hBhIGf+TXITpSRGQQN+J0BH0w5yDDwM2OM+/odNmhtZ+UYRAFC159+vRRU6dO1ekzgeUeESGnbppO2MM/JveUgcGNCTmDBrvpBQoUUEWKFHGNgcmN20kagVl8VqtWTbVq1SoUJzul3bp1U7feeqteGJtIEQ0hLvy6mUkZVwYhRImM8QPyRTj7ZIp06tevr3r16qX9spk4vV7BDl0r4rUTaVFH9lMsxJyY6DF5pG7AiFNC9DXYJafeTR6Jj3vCmXviZ0JL2exlsKcb6d4PLxG3PU3iNPkiz9xD6OxQR+DMRJDnOBHGMA48yCSRHWE/RLqczKG/gdNiCAwWLFigT2Ptiyqv+fTDS6THhgt6rJSVtGmD7777rjaIkKj+IOWLxPPJwDNb6t1rO0IfjL4GkUgWb3yHLznaPtIPPEPckD4VrA0fx2pH6AwhDUH/Qj8DccVSMHp7ZiNIv/DwD9756KOPVM2aNbXOHsZc+GPiyUYTGznRFqZuSfgpu/neiGAimcDmhhuhO4f4pTEeRZ6NJEMQm13ReN7khwUTp4qcBFGH9HHckw9DXurI9FNe693EHesK9oj7YmEZNQj4jj/uWRCz6WTIa79kwnspuwnr5wrPsxhzbrYShx9egvcRh2cDFd96bdq0UcWLF9dje6dOnVxPL/3kk40nxgnGDeo+UWITJhUbpYnmU74XBAJHIIKrA3mcAgQ2b95s1atXz8qXL59VpUoV7U+tT58+2tcZ/rIg/PIMHTrUKlKkCLNo7ZsJP1YbNmywSpUqpf0dbd++PSy3+JAZP368/qZQoULWoEGDwny6mcDLly+3ateurdMnnPGZZ977ueJnhnziQw//RsRXuHBhq3///mF+xYgTnzlFixbV5aFM9j+nnxp8RU2bNs0qU6aMDnfXXXdpfzng4qRdu3ZZ3bt31+karObNmxfy0WR84Zj0jD9Ae37IVySfPs70+B0pTsph0ovCPXYAACAASURBVLHHaa8b6h388bUF9vj3o97Jl/mWePC5xG/8AOKDjSu/o/k6c8urV16yp09alNH4kDL5sqdNvPgwgkcNH+HPqVmzZtbkyZO1bz23/ER6BkbwDXz0yCOPaP7Mnz+/9kFImzHkN59eeQnM8Zc1Z84c3S4pM+3T+Eo06fu5OvnE4GjnDRNf0HhmQ7078TFtM1I7wm+gve96/fXXNZ8OGDDAKliwoOZHO394bUfw+cCBA0P9remXTd34udr7FVPf9mvfvn39RBcK66XstCE7/fHHH1bXrl1z9MWEOXLkiEVenH4daS88w88Z/bpfctapKbsbz5u48ZeHH1IzJtI3OilWHcVT7840Iv0GV8Zf0y/R3+HbEP+hhuzpe+k/zXdeym7CxrqaMcNgbq5OH6leeYl66NChQw4ftvgzbNu2rTVmzJhYWXJ9b2/fJo9c7eOL64dRHu7Zs8eqW7duwr4VoyQhrwSBjEXgOHIW+EpRIhQEBAFBIEsRYCeaU1NOaNhBFxIEcjMCnIhhdAaDRLgQsRMnd+3atVPdu3dPqi6pPU25zzwE8BeLhAk+Yjn5tROn0ZzkZYqBNfKJMR34Wfpve03JfV5AQMQv80ItSxkFAUHAMwKIzSFOi2iVkCCQ2xFAHB+LmFgB3rp1qxahZq8XUVHEoBFJtfurzO14SPlyIoANADa7MLpkxP65IhKP64tI6hU5Y0ruE9RUUGHAEq4s6JKLtcSemQjIoi4z6yXtucIHHEYp2JWL9Udnn5soW8ou+Qye67p27aoqVKighg4dqp0Mg7EbES5Wu+A9JuztOpVucfl9JvXuF7Hgwiej3pMRp58Sc8qCexcsGdapU0ebq8dk/WOPPaZ1sTmpw6BUuvPpp0xewko78oLS/4a55ppr1Pvvv69mzJihrafSt2FFFWvEHTp00I7tMwFP8oS+XyZZbfWOsoQUBBJHQMQvE8dQYhAEBAFBQBAQBAQBQUAQEAQEAUEgbQjISV3aoJeEBQFBQBAQBAQBQUAQEAQEAUFAEEgcAVnUJY6hxCAICAKCgCAgCAgCgoAgIAgIAoJA2hCQRV3aoJeEBQFBQBAQBAQBQUAQEAQEAUFAEEgcAVnUJY6hxCAICAKCgCAgCAgCgoAgIAgIAoJA2hCQRV2A0OPTp3r16urxxx9Xv//+e4AxS1SCgCAgCAgCgoAgIAjkPQTGjh2rLrvsMjVmzBjtciPvISAlFgS8ISCLOm84+QpFB/Tzzz/7+kYCCwKCgCAgCAgCgoAgIAjkROC///2vmjlzpjp48GDOl/JEEBAENAInCA7BIVCgQAH13nvvaaeXp5xyiq+IDx06pJYsWaLw9XLOOeeoypUr+/o+nYH/+usvtWzZMrVw4UJ13333qdtvvz1qdkxZ+e77779Xp59+unrxxRejfhPES5PuvHnzPOUziDTzYhybNm1SOIFdtGiR9tMGPyeDSAe+W7lypWrRokWudTabKjwTraNsyWei5ZTvBYFMR4C2uGHDBrVt2zb13Xff6fEVX3PZStWqVdM+E6dNm6bwqygkCAgC7gjISZ07LnE/pRO96KKL1A033OArjsOHD6v58+erN998U7HYySZiB2369Omqf//+nnbRKOvSpUvVDz/8oP/4PhVkMB4+fLinfKYiT7kxjd9++02NHj1azZo1Sx07dixpRSSdzz77TG3evDlpaQQd8T///KNw5nzPPfd4Ps1PFZ6JljVb8ploOf18b1mW7uMQyT/zzDPV5Zdfrl599VW1e/duP9Fkddh4eD6rC5wBmactMp9gw2vFihVJ7YeDKC7jxJdffqk3ht3iO3LkiC7HY489pk466SS3IPJMEBAElFJyUhcgG3DKNm7cONWyZUvfpwZnnHGGeuGFF9S+ffsSzhHiCaNGjVI1a9b0nY94Er/wwgtV06ZN1dy5cz19TlkbN26sw5LPX375xdN3iQYi3YYNG6o1a9YkGpV8HwWBu+++W582d+rUKUqoxF+RztGjR9WIESMSjyxFMZx44omqfPny6vrrr1cXX3yxp1RThaenzEQJlC35jFKEwF/RJ8KfbNZ98sknesOub9+++mR5wIABeqEXeKIZFqEfnkdyg4Vw8eLFM6wUyc9OkGWnLfK3a9cufWKX/NzHl8LixYvVK6+8orBHgKTS7Nmzc0TEZuzgwYPVHXfcoUqUKJHjvTwQBASBfxGQk7p/sUj4rnDhwnoXPn/+/AnHlUgE+/fvV+zUCQkCgkBmIXDcccepe++9Vz355JMp2XDJrNLnvdwgwXDWWWepCy64QFH3nNY1atRIi8VxkpIXyA/Pr1+/PuNPlZJVZ3mx7EWKFFEff/yxev/991XRokVdoWVTgE3jBx54QLch10DyUBAQBDQCsqjLZYyAKOPbb7+tfv3111xWstxRnD///FMNGTJE/f3332EFwrAO+gKGEFnaunWr+SnXXITA3r171caNG3NRiTK3KOluR5yWIC7GaYOhs88+W1111VU5+gDzPjdeY/E8p3PoZHN6GQSlu979lCHosvtJO91haRvnnnuuFktG4kdIEBAEEkNAFnWJ4ZfQ1+hVoF+BnsXNN9+szfUyGDkJ0YQePXqo6667Tu9UcX3rrbfCJgUsEhBpRMSwc+fO2mDLaaedpsMj7oUYhpN++uknVadOHb17TPqDBg0Ki9MZ3utvdOUeffRRHS/xJyJeyYDHbnfZsmV1WZgkoa+F0RMn8Yx3hGF3GKMtfEsckQhcwIfw/CEO6oc4FQU3sMcACyI0Dz30kC47ejRgbKhr166KU1wMiBgy9YYS+44dO/TjOXPmqJNPPlnHi6gWOpqvvfaa1o8AV/jl008/NVH4unrhJcpAXuFNXHO88cYbOk34DkNA6DfYyV5HnEQ8/fTTCeu52eOkXki7devWekGMyKWTqEfTlsAnVj6J042X0OswZYdvEdelTPDSF198EfcpguEzzHJfeeWVUUVG7WUPCs9Mr3c/7Yi6RwdnxowZqkqVKrp+wIn2hq6uoXjaEX3I0KFDdX9MnPRf6CTFSw8++KDmp1NPPTUUBbxAu0KyIx7yUnbDb6Zfg6ch+3P6LPvm0pYtW3TbpdxG9++DDz7QWHD1SyatWDxPPR1//PHqzjvv1Hpg6JuafLv1x7HqKJ5691M2NmToF1iM8IeOrF1H0k//6bfsfvLpJawXXjLx0O/jUoC5AvXDmIylb/r7cuXKiXSQAUqugkA6EbCE0oLA/v37raZNm1r9+vWzDhw4oP9Gjx5tFStWzBo5cmQoTzt27LCqVq1qDR48WIfhxe7du61nnnnGGjJkSCic/Wb27NlWo0aNQuHt78z96tWrrVq1alkLFy60jh49au3du9fq1auX1apVK+vgwYMmmOfrzp07rXLlylldunSxhg0bZlG+w4cPW8OHD9f5pxxuRFn5JhKBSfny5a3Fixdbx44ds7Zt22Y1aNDA6tGjh47ffEeeO3furN8RhrBLly61SpYsaS1ZssQE05iADRgZWrNmjfXSSy9pXM0zr1fSnT59ui57+/btdX2CJc+7detmtWjRwjp06FAoOvBw1g04tW7dOqzewYS8r1y50tqyZYuOnzD79u2zJk2alCOOUAJRbrzy0saNG63evXtb1apVs9q0aRPikRUrVlj333+/tWjRorBUZs6caT388MPWqlWrNC+tX79e82flypUt+CIemjVrltWkSRNd33wPpmDSuHHjML6mHslnu3btQvlct26dVbZsWWv+/PlhSXvhJdrFyy+/bFWsWFG3BcoCL3GtVKmSNWrUKP07LGKfP+CBaDwfNJ7ZUO9+2xF1SX38+OOPuj7ow6ZMmaLrjfZvyE87oh327NlT94PwG3HSP8LbXBMl4oOPmjdvbo0bNy5uPvJadjCFlzt27BiWFv0dfQl9tCF4pEaNGtaECRN0v8o7viOvU6dOzdHmzXder7F4nngYB519szN+r3Xkp96daUT7vWzZMqtEiRIhnMgz4w54mn7eb//ptezR8hXpHf1v9erVrbVr17oG8cpLfEzYhg0b6j6ZPnHBggVW6dKlrYkTJ1pjx44N4yfXxKI8NPMH+7gcJbi8EgQEgQgIyEldmlbUU6ZM0Urz9erV07o17OQ+8cQTqkKFCmE5wnDKgQMHtLU8s9uLifhSpUppvYywwB5/IKKJ4v7zzz+vlY/ZJWV3tkGDBmrnzp36RMhjVDmCYaSlVq1a2k3BCSecoE+tOFmx76Dn+CjCA8QPMS7Qs2dPddttt+ndwYIFC6qOHTtqM832OFGwRum6S5cuijDsJBrdxmjWEdkxnzBhgmrfvr027hEhKxEfc6LGiQ8729dee63GFCx5zikCZbBbM3UzxwxOnLA6iZ1QrKhS77h9ANd8+fLpkx7S8EteealQoUK6TPBd8+bNQzxy4403ar5bu3ZtKGnESRH3bdKkic4rvMRJFMaC/Lr1CEWqlK7LqlWr6rrkOeXlpA6MnX6KyGezZs1C+eTUs2TJkmGnpF55CQMmFStW1FmhDJQFXuLatm1brf/BCUQi5MYDJr5k4JkN9e63HdGmcZ1i6ge+4zftw2lsyms7wlorusjPPfec5jfixDgDOnDo/bhJUZh6i3XFiBZ9GHpBt9xyi+7n4at4yGvZwbRMmTKKEzjaiKHt27cr2hZ9iiFckPCb8PRH3DM2cQJFvmO5qTHxRLpG4/lI37g991NHXuvdLR23Z2bcfPbZZ1WlSpU0TvTNjDOc6ptTTz/9p1s6qXzmlZcoGz7iGIPM+FqsWDE9vu/ZsycHP6WyDJKWICAI/IuAWL/8F4uU3TE5YBGCJSf75JxB3jn4oXuBSJghxJQYpBHrc4Y1YWJd0bdjoUHcdiIvDEjod8U7iCOGwaTAEPojiGsgcogokh9ikYaBAad/HcQRWUjhf4dJF4vGb775Ri847HL5l1xyiZo8eXJEgxSIpLEgQQmbyWCidMUVV+gFgImH+mRiGC9Rv2bih+4Bk7REyC8vgR9GHgy58SeTGXjppptuMsH0NdGys3hD1AexSyYREOVn8YjivJ285NMrL5l4nXHy3NQvbkvOO+88EzTQazLwzKZ6t+NsgHXjJRb4hhALY6GN9Tz7Bop576UdIfKK+DT9ibOdUe+IRjOpj9ecOqKW5I9F0ocffqjFOnEBQ//ml/yUnU0h0sQIB/0wfSULTERVYxHtzN6Xxwqf7Pd+68hLvfvJMxiy6MfQkemX+R5RzLp16+bYmHP2IW79p5/0kxHWDy+5pY+Kh5AgIAhkDgLxzzgzpwxZlxMGVnZOzWQ1VgGYTAwcOFAvZFgYsRBhgImXOI1jwszElIHG/LEAQR/PqTPlJx3nQtMMZPHEiWNpzL6bE0qTD+JkosXkhB1EFslMXJyLVMKxWHObmLCb369fP724TgRLk6dsuQbNS5yagbF91z8ILNgc4DS6dOnSWneDkxLqjAl3PAtlr7wULe+kjcELt4VDtO/8vEsWntlS716xYoJPnaLPw4YCE+tvv/027ETKa1yEA3c2u7BKavpDc+X0lj4T/aNEiPjQweIkkMUc+mKUwy/5KTubXGzQff311zotTmZYrCHtYSc2UeARNsfoq+lX0SG9+uqrFRtzmUCpqKNo5aTd09c58WCh7yZpES2uTHnnlZcYg+mP4VkWtnzH6S6nd2yECAkCgkBmICCLujTWg3NwcMsK4oFPPfWUQmQGMUF2k9955x1t3MMtvNdnTJqZqNA5O/8QschG8oKnKRe75YifInaJxTVO7XI7JYuXwN15epYolizEa9SooU83MICCuDIn21999ZXm10Tjz+Tvg8Yzm+rdS73QX2EQicVR7dq1tbj4559/rkV+4zn5sqc5cuTIHP0h6cF3LMiCIDYHMAqCESdE1/yQ37KzkGRjhPbDGMLC1810PBt8iIuDKxiykEUiBNFT50adn/wmI2wq6ihSvoNum5HSScVzv7wEH7G4Q02EjTWkJlDZYENASBAQBDIDAVnUpaEeGGgZKBk0Y9HEiRO1+CGLj/PPPz9WcE/vjaghopzJJk4l2fWNZyeTwQIdIzddFsShEBVlkAFLxEAimYmnnM6TQnSwmLxg1ZATvmHDhmnRpGTj4XZqmOw0TfzJ4CWw54SDOk4GmR3i4cOH69NlnNBy2uCXvPJStHjhRSioyb1bWsnAMxvr3Q0b84zFCVb3EF/k9CCINsVpC7pRxgKtSSuRK5Nm8on1WDdrvfRJfk//4ik7op9suiDyT9txm4SvXr1aoSuLo3REWelLsbhr9JITwSGob5NRR37yho4wp1ScaDqJukzFeOpMN5HffniJcRwLqJyMszEAb3P6K77jEqkB+VYQCB4BWdQFj2nMGBkcUD5fvnx52GKDSSN6F3ZCf4cFUaSJC5MCtwmDPQ7eT5o0KfQIox7sOKJn5KS5c+dqsUbnc6+/7Qr5fIPIHHpCGK7wS+xmb9iwIYe/NnDCcS9K/RCDPWawER1yGklgAB4/fnyOBRuTZwhc2W1kgMIUdbIJPQsGSAZFQ+QZPcZkUxC85MwjC2v42bnQWrVqlV6QO8N7+c0iHt60Y8R36AdhZIITZr/klZdMvPCxcyMAnkNk+tJLLzXBAr8mA89sqXevYNJeqJ9o+jz0eX4WTPQH9FH0f2bxbvJDH8LijHbrhxAXpE+i/7PnBb6i7+fEzGyweY03nrKzCYEOMhtZ9D+R0uTksHfv3jrPydyo8VpWwq1cuTLkEicZdeQnLxjlYZPJOUYTB32D3X2Nn3gjhbWXPVKYRJ775SX4GZUFROExUkY7sfN1InkJ6lskbqpXr651RpFQEBIE8hoCsqhLU41jPWvTpk1q3LhxevLIxIHOkokKzqkRtYQYSHBKjfw6REeKiAziRpyOoA/mHGQYuNlx5h2dLju09pMyDKJgwatPnz5q6tSpOn0mGtzjY86pm6YT9vCPyT1lYHBjQs6gwW56gQIFVJEiRVxjYKLkdpJGYBaf1apVU61atQrFyU5pt27d1K233qoXxiZSREOIC79uZlLGlUEIUSJj/IB8Ec5++kc69evXV7169dL+o0ycXq9gh66Vc9JHWtSR/RQLMac1a9boySN1A0acEqKvwS459W7ySHzcE87ckycmtJTNXgYvefXDS8RtT5P4Tb7IM/cQOjvUETgzEeQ5ToQxjAMPMklkR9gPkS4nc+hv4LQYAoMFCxboEwT7osprPv3wEukx6UaPlbKSNm3w3Xff1QYREtUfpHyReD4ZeGZLvXttR+iD0dcgEsnije/wJUfbR/qBZ4gb0qeCteHjWO2IUz+kIehf6GcgrlgKRm/PbATpFx7+kUckAegbjJgleUL0m9MOjGuwIeWH/JTdxGtEMJFMYHPDjdCdQ/zSGI+qWbNmSJIhiM2uaDxv8sOCiVNFsKEO6eO4t4vUeqkj0095rXeTfqwr2CPui4Vl1CDgO/64Z0HMppMhr/2SCe+l7Casnys8z2LMudlKHH54Cd5HHB5+xbdemzZtVPHixfXY3qlTJ9fTSz/5ZOOJcYJxg7pPlNiEScVGaaL5lO8FgcARiODqQB6nAIHNmzdb9erVs/Lly2dVqVJF+1Pr06eP9nWGvywIvzxDhw61ihQpwixa+2bCj9WGDRusUqVKaR9d27dvD8stPmTGjx+vvylUqJA1aNCgMJ9uJvDy5cut2rVr6/QJZ3zmmfd+rviZIZ/40MO/EfEVLlzY6t+/f5hfMeLEZ07RokV1eSiT/c/ppwbfTtOmTbPKlCmjw911113aXw64OGnXrl1W9+7ddboGq3nz5oV8NBlfOCY94w/Qnh/yFcmnjzM9fkeKk3KYdOxx2uuGegd/fG2BPf79qHfyZb4lHnwu8Rs/gPhg48rvaL7O3PLqlZfs6ZMWZTQ+pEy+7GkTLz6M4FHDR/hzatasmTV58mTtW88tP5GegRF8Ax898sgjmj/z58+vfRDSZgz5zadXXgJz/GXNmTNHt0vKTPs0vhJN+n6uTj4xONp5w8QXNJ7ZUO9OfEzbjNSO8Pdm77tef/11zacDBgywChYsqPnRzh9e2xF8PnDgwFB/a/plUzd+r06eo33gu9LOx37j9FJ22pCd/vjjD6tr1645+mLCHDlyxOrbt28Ov47kHV+P+DmjX/dLzjqNxvMmbvzl4YfUjIn0jU6KVUfx1LszjUi/wZXx1/RL1Ce+DfEfasievpf+03znpewmbKyrGTMM5ubq9JHqlZeohw4dOuTwYYs/w7Zt21pjxoyJlSXX9/b2bfLI1T6+uH4Y5eGePXusunXrJuxbMUoS8koQyFgEjiNnga8UJUJBQBAQBLIUAXaiOTXlhIYddCFBIDcjwIkYRmcwSIQLETtxcteuXTvVvXv3pOqS2tOU+8xDAH+xSJjgI5aTXzt99NFH+hQ7UwyskU+M6cDP0n/ba0ru8wICIn6ZF2pZyigICAKeEUDkEpE5RKuEBIHcjgDi+Oj3YQV469atWoSavV4MpiAGjUiq3V9lbsdDypcTAWwAsNmF0SUj9s8VkXhcX0RSr8gZU3KfoKaCCkPLli1lQZdcqCX2DEVAFnUZWjHpzhY+4DBKwa5crD86+9xE2VJ2yWfwXNe1a1dVoUKF/9femcDvVG3/f2ueNGikoll1k0rFpaJUupFSkVLiGqNJNNxrSFxuEhFFXZRL6E+UuhqUoYwhkXnIVKZkSiLD+b/eu99+2s/5PsM5z3Oe8bvW6/X9nvOcs88ePnvtYe219tpq0KBB+pBhMI5EhIvXLnjfvHnzsD2VkeLy+0zq3S9iwYVPRb2nIk4/JWa/FMe74Mnw4Ycf1u7qcVl/zz336L3YaOpwKJXpfPopk5ew0o68oPRHmAsvvFD95z//URMnTtTeU+nb8KKKN+IOHTrog+2zAU/yxH6/bPLa6h1lCSkIJI+AmF8mj6HEIAgIAoKAICAICAKCgCAgCAgCgkDGEBBNXcagl4QFAUFAEBAEBAFBQBAQBAQBQUAQSB4BEeqSx1BiEAQEAUFAEBAEBAFBQBAQBAQBQSBjCIhQlzHoJWFBQBAQBAQBQUAQEAQEAUFAEBAEkkdAhLrkMZQYBAFBQBAQBAQBQUAQEAQEAUFAEMgYAiLUBQg9Z/rUrVtX1alTR23atCnAmCUqQUAQEAQEAUFAEBAEBAFBIPsQGDlypCpZsqQaMWKEPhYl+3JYOHIkQl0K6hnmXrFiRQpiligFAUFAEBAEBAFBQBAQBASB7ELg119/VZMmTVJ79uzJrowVotwcVojKmvKinnrqqeqNN97Qh14eddRRvtLbu3evmjt3ruKsl5NOOknVrFnT1/eZDPzLL7+oefPmqVmzZqkbbrhBlStXLmZ2TFn5bsaMGerYY49VTz31VMxvgnhp0p0+fbqnfAaRZmGMY82aNYpDYGfPnq3PaYOfU0GkA98tWLBAtWrVKm8Pm00XnsnWUa7kM9lyyvf5jYAZJ3JxLM7vmsmv0tFfrlq1Sq1fv1599dVXeg7EeYC5SrVr19bnWo4fP15x9qVQZhAQTV3AuNNAS5QooS699FJfMe/bt0/NnDlTvfLKKwphJ5eI1ZkJEyao3r17e1qhoazffvut+uabb/Qf36eDDMaDBw/2lM905Ckf09iwYYMaPny4mjx5sjp48GDKikg677//vlq7dm3K0gg64t9//11xmPN1113nWZufLjyTLWuQ+aR/uOyyy1TPnj3VgQMHks2afF+IEfDLS2acyMWxONFqTqRfSjQt+e4PBOgvmfOxKPndd9+ldKwMAnPG8v/973968T5SfPv379fluOeee9QRRxwRKYg8SwMCoqkLEGRW9kaPHq1at27tW2tw3HHHqSeeeELt3Lkz6Ryh+n7nnXfUAw884DsfiSR+xhlnqJYtW6pp06Z5+pyyNm/eXIcln99//72n75INRLpNmzZVixcvTjYq+T4GAhUqVNDa5o4dO8YIlfwr0mHCP2TIkOQjS1MMhx9+uLrtttvUJZdcos4880xPqaYLT0+ZiREoyHyyNwPt67XXXhtz1TfdfV2M4qf9VWEuux+wvfKSiTPIsdjEme1XP/0S1jWO46i//vWv2V6swPMXZNnpL/n7+eeftcYu8MwGFOGcOXPUP//5T4XPCKzJpkyZUiBmFkLefPNNdfXVV6uKFSsWeC8P0oeACHUBYl26dGm9Ch9glAlFtWvXLsUqkJAgIAhkFwJFihRR119/vf7LrpxlV26KFSumGjVqFDdThbmvK8xlj8sYVgCvvGR9Uuhu/fRLK1euVOecc06hw4gCF8aylylTRg0bNkytXr06ap/MogAL+0KZR0DMLzNfB4HmAFPGvn37qh9++CHQeCWyYBDYtm2bGjBggPrtt9/CIsSxDrbohjCH+fHHH81PueYRAjt27NADZB4VKfCiYMqzbt26mKboQfZ1aB5YiU6XKXiygAVZ9mTzku3fe+GlbC9DOvIXr1+ijbBvvk+fPoFkJ5fGuKDLHgiAaYoEU8qTTz5ZC/JYZQllNwIi1GWwfrZu3aratWunG8vll1+uXcHS0bmJycaLL76oLr74YsWKGtdXX301TDBASMCkEdORF154QTtsOeaYY3R4zL1Q8btp+fLl6uGHH1bHH3+8Iv3+/fuHxekO7/U3e+XuvvtuHS/xJ2NeSWfKfr2bb75ZlwVzBfZrsZndTTzjHWHACactfEsc0QhcwIfw/GEO6odYLQc3sMcBC+YZd9xxhy47R1uAsaHOnTsrVo1xIGLI1BsbpDdv3qwfT506VR155JE6XvYUsUfzX//6l7a9B1dWSUeNGmWi8HX1wkuUgbzCmxzN8dJLL+k04TscATFJssmuI3jp73//e9L73Ow4Dc8/88wzWiCOtMeKejRtCXzi5ZM4I/ESewZM2eFbzHUpE7z04YcfJrzvwfAZpmDnnXdeTJNRu+xB4Zkr9U77o8xYPdCO2KPsJtNmgurriO+RRx5Rt9xyi/rsQSoTrQAAIABJREFUs890X4PZJ/0YfAQ/PfTQQ1roc+fFy2+v/ZKXOgq67Hb+EaJpu+BPmWlPAwcO1GMDV0M2f0ZrR/R73bp106v39K9sTWCveIcOHfQE0T72h/ZGPPxxD9EHmmdXXHGF/t6k7/XqhZdMXF7HYhM+3pX9RxMnTlS1atXSeIIpZWZ/nyHTJ5hymrLbzxlXqHNDXuvIhI93NWnF65eoj0MOOUSbQ7MPjD3BJt+Rxkx4ftCgQZp3KDvzAPaNGUrlGEcaaJXouxFG+GMfM3VsyM8Y57fsJo2grl54yaTF2MyRAsznqB/mTXhjp11Xq1ZNLLgMUPl8dYQygsCuXbucli1bOr169XJ2796t/4YPH+5cc801ztChQ0N52rx5s3Pvvfc6b775pg7Di61btzqNGzd2BgwYEApn30yZMsVp1qxZKLz9ztwvWrTIqVevnjNr1iznwIEDzo4dO5zu3bs7bdq0cfbs2WOCeb5u2bLFqVatmtOpUyfn7bffdijfvn37nMGDB+v8U45IRFn5JhqByW233ebMmTPHOXjwoLN+/XqnSZMmzosvvqjjN9+R5xdeeEG/Iwxhv/32W6dSpUrO3LlzTTCNCdiAkaHFixc7Tz/9tMbVPPN6Jd0JEybosrdv317XJ1jyvGvXrk6rVq2cvXv3hqIDD3fdgNMzzzwTVu9gQt4XLFjgrFu3TsdPmJ07dzrjxo0rEEcogRg3Xnlp9erVzssvv+zUrl3befbZZ0M88t133zmVK1d2Zs+eHZbKpEmTnDvvvNNZuHCh5qWVK1dq/qxZs6YDXyRCkydPdlq0aKHrm+/BFEyaN28extfUI/ls27ZtKJ9Lly51br75ZmfmzJlhSXvhJdrFP/7xD6d69eq6LVAWeIlrjRo1nHfeeUf/DovY5w94IBbPB41nLtW7gZI816lTx1myZIl5FPEaVF9HOuXKlXO6deum2+7rr7/uXH/99Q58uG3bNt1X2n1GxMxEeOi1X/JaR3YSQZWdOEn//vvvd8aOHav7Vfrv559/3nnyySedzz77LKzNe2lHP/30kzNx4kRn0KBBToUKFfT3nTt31n0EbezKK690pk+frotD+6JPa9q0qfPrr7+Gikg/Y+oj9DCBm3i85HUs9pM0GNFfLFu2TPcXjLGffvqp7lcYnwzBH/Q3YA0OhhiT6O/JmyE/dWS+8XON1y8RF3MV9/jpToPxjHpjPkG/TdmZZzBGcDWUijGOuOfNm+dUrFgxxMvkmbkBeJqx2O8YR7xeym7K5ufKGFm3bt2ofZ1XXiJNwtKOzBzo66+/dm688Ubno48+ckaOHBnGT37ySFgzx0ukH/SbloRPHAHR1GVIYv/000/1ymXDhg21M5Ojjz5a3Xfffer2228PyxGOU3bv3q1XxggD4SK+SpUqEVexwz6O8gPTHTx7Pf7443pjKytwrKY1adJEbdmyRWuEonwa9zGb9+vVq6ePKTjssMP0ajuaFXuFMm4k/xcA88N3331Xr/heddVVeuWpePHi6vnnn9cugO042bzLht5OnTopwrBKhVYMiuUdEU3U2LFjVfv27TWuXvNmwqFRQ+PDyvZFF12kMQVLnrNKSxlsb6aRXP2CE1oHN7HKhhdV6p1jH8C1aNGiWtNDGn7JKy+VKlVKlwm+e/LJJ0M88pe//EXz3ZIlS0JJY06KuW+LFi10XuElNFE4C/J7rEcoUqV0Xd577726LnlOedHUgTE8ZhP5fPTRR0P5ROtZqVKlMC2pV17CgUn16tV19JSBssBLXJ977jm9t4DV7WQoEg+Y+FKBZy7Vu8EBPuIvWfLT15mjZGi78BvaCPjoxBNP1JrDRPLitV/yWkd+8uCn7BxBQh9TtWpVRX/EPWMT2g36XnNMjdd2dMopp+i+AqcJ9G2Md40bN9Z9BBp/POOiDYVoX9dcc42+xwTQEGmRH+ojGYrHS17HYj95YMwBM9N/kAd+039T14YoG2VEA0c/Zmjjxo2K/o96MOS1jkx4v9dY/ZKfuKhb9vQ/9thjuh1RdhxoNGvWTPeftjVS0GOc4Xk07zVq1NC8zPjJXADLC6P19DPG+Sl7KsJ65SXKxhlxzBPMHIh2hcXD9u3bC/BTKvIqcWYeAXGUkoE6oFNjsGfAsyfnDG7ujvX888/XbmRNNjH3YwDAvMUd1oSJd2W/HYIGcdtEXujs2N9lBnH7vZd7VPxMCgwxIcIUAJNDzJv8EELa6aefrtxnt2COiCDF2S4MFgiNX3zxhZ5E2DbfZ511lvr444+jegDF3AmBhA2+DLbJ0rnnnqsnKCYe6jOZiSn1SxwQdu3JTm788hL4nXDCCaY4Oi9unmOghJdwP29TsmVHeMOMhAkgAxRE+REe2ZRtk5d8euUlE687Tp6b+sUkkElrKigVeOZSvQeNqZ++jrZq2hv5YDLo5nc/+fPTL/mtIy/58FP2SPHRzuy+nDB+2xHfsFBBP236ZjB297eYyGECuGjRIt3eMd/jDK/KlStHylpgz/yMxX4SZQHKECZxLAThOdBe4DPvWbhDeMYJB2MlfIO5Kuaa8ShSHcX7JpXvMc1lGwL17R6v6D/ZYoDgZVzeBz3GgSECJc6o7LaMKWaDBg0KLJ66+3m+SabNpwJbP7wUKX224QgVHgSSXwotPFgFVlI6bVblzGQ1XsR0gv369dOCDIIRggidV6KENo4JMxNTOjHzx6SG/XjuPVN+0nF3iKaTTCRODpbG7bvRUJp8ECcDBAMfq1MMzAyKbiGVcEwe3BMT4mG1tFevXlq4TgZLk6dcuQbNS2jNwNheUQ4CCxYH0EbfeOONel8A3reoMyYKiQjKXnkpVt5JG9fNkSZmsb7z8y5VeOZKvfvBykvYVPZ18dL32y8FXUd+ys4iCumzOEZfTb/KHtILLrhAaypNWRNpRwhsLBbGIvpqxja0PIyPLJzQr9iLSrG+T/Sd37HYazoIN2DFXiYWvBAqvvzyyzBtnIkLYZdF1M8//1zv/0Yzg7CG5tgmr3Vkf5Pue/ovFhMefPDB0LyCuuUPKwh4MpVnl9I3wzcsJtuEEBnJGsYOk633XnmJeRJjJnscEWz5Du0u2juEbKHCgYAIdRmsZ3fHEykrmAeySR9zDMwEWQV77bXXtHOPSOG9PmPSTAdLw3f/ob7PRfKCpynXW2+9pU0lMbvEmxdau3ynVPESuLu1Z8liiSB+//3369VtHDZgIoVm+5NPPtH8mmz82fx90HjmUr2nol4y3dd56ZdSVUdey84CH+biOJrCOoIJOBYhmMy5F+pSUUfEaZxFIRTgVKNs2bKpSqpAvF7qqMBHUR4wnoIj5of169fX2xk++OADbZIOtm5C4GHxij6OcR7hL1LZs6GO3HmP9nvo0KEF5hXgQv+NkJ9KCrr/TGVe48Xtl5fgI4Q7tvKw+IllC9tqWBAQKhwI/GknVzjKmxWlpBNnoGTQjEcfffSRHuwQPiJpnOJ9H+k9K1kQppyp7mBZCWXV99RTT42UlZjP6IjwtMWKtzHXMB9gxsHqLx0Y7zExwOMVXgrdRDnZ32Xjxx4sBljKz+r022+/rZ566qmUT2DsPLjzmerfqeAl+JhJGHWcCjMPs/rICuSCBQu09zwmnPFW/t1YeuUl93f2b8zIoFS2mVTgmYv1buOezH06+zp3PqlLr/1SKurIT9kxe2Sv7JAhQ2L2gUG0IzdO5jeaKbye4m2QRbZUm16Srp+x2OQz3hXBDI+DvXv3VldeeWW84Po95WZhjG0ZaOrwcuwmr3Xk/i6dvxmn2b9mPDmnM23SYpxHS4XW2T0eoYFGk5hLGjs/vMRcCy+1aIexQhIqnAiIpi4D9U7Hw+bz+fPnh5k6MmnE7t4mzFDohKIJA5gysP8gFvF+3LhxoSA49WA1i/0Rbpo2bVpC7qNNPPZmb55hMsc+IRwO+CU20rOvgg3zNoETwh4bzCEGEpwaIJzZm9B5R+c+ZswYbdJjx8GECwJXVrIwfcHNcaoJG346X1bgDJFn9jGmmoLgJXceEa7gZyYiNi1cuFDvpbGfeb1HSIc3bYz4lr0nuDdHw+yXvPKSiRc+dpsMw3OYTJ999tkmWODXVOCZK/UeBJjp7Ovi5ddPvxREHSVbdo5/efnll3U/ahZq3GX0247c38f7TZ+OYEmfkmrTS/LiZyyOl3fznv6c/sMtVJj3XKkr2wyRhSL2ibPYyBhhBHL7G+691JH7m1T/ZrGNMR5iXGWsZx5hFsFM+ozFCLuMf6kiHNOwEOieR5Ee/bd9xFAQebDLHkR87jj88hJCKwId2xVwJEcd2Hzmjj8Tv1mwqVu3rt4zioWCULAIiFAXLJ6eY8Mz05o1a9To0aP15JEOj4bIQMDh1JhaQnRSHEqNbTREI8W0A3MOtCPsB3N3YAwKrJTxjgbN6p+9OoVDFLxD9ejRQ5/LxOSVP85o4mwm9940nbCHfwyQlIGOkwk5HRKrlWjpypQpEzEGOng0ae4JNIERPmvXrq3atGkTipNVuK5du+oVUARjQ5gdEBfnupnBhCsdHJods2mbfBEOwcEQ6TRq1Eh1795dn8tmnnu9knf2WrkHK9KijvgzhAnN4sWL9aBH3YARWkL2AjCZod5NHomPe8KZe+JhwkDZ7DKY+GNd/fAScdtpEq/JF3nmHmI/CHUEzkwEec4BtTjGgQeZJLLa6IdId/DgwXpvgPGGBwZff/211sbaQpXXfPrhJfLKggv7WCkradMGX3/9db3ZPtn9g5QvGs+nAs9cqXebR8CcCYp7kcgOw31QfZ3hbcPX1JHhf9o39QUv+CWv/ZKfOjJ5CKrsxMfeOcwvjfOoBx54QFsyYPlgL3b5bUduXE3eo13ZK40jrCD3AMXjJa9jcbQ8u5+jcWQsxNQQ4Q3+wZyUsQnrHJ5hasmYb8iYYGI9YryCmnfm6rWOTHi/11j9kokLgQmtIiailINxiHvbrBSritNOO02P04zXEFc8brO/EMHP8IVpY9SRuSd8omMc2GPyihdstqqAPX/cIxCzMGjI69hhwnspuwnr5xqLP/3wEriyZYHFJLTdzz77rPrrX/+q518dO3YM4zc/+TNhWXhiLGdsp+6TJQT8dCxmJ5vPnPs+8dMQ5MtkEVi7dq3TsGFDp2jRok6tWrX0eWo9evTQZ51xlg/EmS+c9VOmTBlm0frsG86xWrVqlVOlShV9RtfGjRvDssJ5N2PGjNHflCpVyunfv3/YmW4m8Pz585369evr9Alnzswz7/1cOcOEfHKGHmfuEF/p0qWd3r17h50rRpycCVW2bFldHspk/7nPQOGMm/HjxztVq1bV4cqXL6/PYgEXN/3888/Ov//9b52uwYqzkMz5P+acFZOeOQ/Qzg/5inc2lp1utDgph0nHjtOuG+od/DnLCOw53496J1/mW+LhPB9+cw4gZ7Bx5Xess87sPJp7r7xkp09alNGc0WPyZadNvJyPA48aPuKsoEcffdT5+OOP9dl6Jg9ermAE38BHd911l+bPYsWK6TMIaTOG/ObTKy+BOWcxTZ06VbdLykz7NGclmvT9XN18YnC0ecPEFzSeuVLvlN+uU4MRV5vfDE5c7faUaF9n1w31zrmWpn8iPyZPkerKzku0+3j9Et95rSM7jSDKTnz79+93evbsWeBcR9oLZz1yhhb9uiEv7chgZtdhrHo0cZMXzmTdvn27eZTw1U8evIzFfjLCuZb22NqlSxfdj/bp08cpXry47i+pP5s424+z/Ohr3eS3jtzfR/tt875dV7F4nfPyOCvWzFsYv9xEGfr16xeat5j5jQln102QYxzxgytzJDN20C9wbh5nvBqy0/cyxpnvvJTdhI13NeO6jTv37nNsvfIS9dChQ4cC5wxz1uFzzz3njBgxIl6WIr635zJ2XqP1yREjcT2kfTdo0CDs/EtXEPmZIAJF+C7nJFHJsCAgCAgCKUKAVU60pqwsszorJAjkMwJoW3DqgUMijhCxCc1d27Zt1b///e+U7iU1aaLRQaOSq866TDmCvmZTHQVdNokvGAQ40xcrIM7xRfNr03//+1+tIc2WdkU+caZDnyNjrF1Tyd+L+WXyGEoMgoAgkEcIYGaHOS1mO0KCQL4jgDk+3hbxAsz+ZdZ5+eNsNcygMaVL5f42zBCZ4LEvFxPraOaH+V4PscqX6TqKlTd5lx0IsB2FBUmcLpmtGVxpUxxPEm0LTLpzz1Yitpm0bt1aBLpUgJ+ghk8+y3MEbJNEW+Ue6R71fD5RrpRd8hk819kmMbFMkOxwkdqEeeY2pQkix1LvQaCYWByZrPfEcuztqz179jijRo0KmbnDv5UrV9YmdJHMAb3F6i0Upo+Y12O+/cknn4TM5e2vM83z2VDvXuooG/Jp11uy95mud6/5z5Z8si2nVatWehsEbRjT00aNGjlstYGyJZ9ecZVw/hEQ88tUSMoSpyAgCAgCgoAgIAgIAoKAICAICAJpQkDML9MEtCQjCAgCgoAgIAgIAoKAICAICAKCQCoQEKEuFahKnIKAICAICAKCgCAgCAgCgoAgIAikCQER6tIEtCQjCAgCgoAgIAgIAoKAICAICAKCQCoQEKEuFahKnIKAICAICAKCgCAgCAgCgoAgIAikCQER6gIEmjN96tatq+rUqaM2bdoUYMwSlSCQPAKZ5s+RI0eqkiVLqhEjRmiX6cmXSGIQBAofAplsR5nuQwpfbUuJQSCTPC81IAjkEgIi1KWgtuiAVqxYkYKYJUpBIHkEMsmfnEk1adIktWfPnuQLIjEIAoUUgUy3o0z2IYW0ygt9sTPN84W+AgSAnEDgsJzIZY5k8tRTT1VvvPGGPlDxqKOO8pXrvXv3qrlz56qlS5eqk046SdWsWdPX9wQ2cfzyyy9qxowZ6thjj1VPPfWU73jkg9QhkMk6SoY/g0Ckdu3a6rjjjlPjx49Xhx56aBBR5lUchjcQeqtUqaIqVKiQ1eVbs2aNWrVqlVq/fr366quvdF9z4YUXZnWe0505U6dB9smZbEeZ7kPSXX/u9ITn3YgU/J1vPF+whPJEEMheBERTF3DdMMEpUaKEuvTSS33FvG/fPjVz5kz1yiuvKCYAiRBxfPvtt+qbb77Rf6xsFTb6/fffVdu2bdV1112XldrSTNdRovwZBB/t379ffffdd+qee+5RRxxxhKco4efLLrtM9ezZUx04cMDTN7kayPQBmKfmQlk3bNig+6wFCxboej148GCuQp+yfKeivSfSjoIsoN8+JNv7ZD/YCM/HRysfeT5+qSWEIJAdCBzasWPHjtmRldzPBVq29957Tz3++OPq+OOP91UgJrmszLNnAU3d5Zdf7ut7AhPH1VdfrQUaJoXbtm1TlStXjhkPGr1169aps88+O2a4XHl5yCGHaBzQGJQvX14dfvjhWZX1ROooqAIkw5/J5oGB/s0331Rly5ZVN9xwgypSpIinKMHrxBNP1N+cccYZUb/BnHPw4MHqkksuybo6j5pp1wvKCj4LFy7UV/YfZjOdddZZuq+54oor1NSpU7V28ZRTTklrlrO93oNu74m2o6AqJZE+xE+fnO3jkfB8fE7KN56PX2IJIQhkDwJifhlgXZQuXVpriQKMMuVRrVy5Up1zzjkpTyddCSAsXH/99fovXWnmSjqZ5E+E65YtW/qGqlixYqpRo0Zxv9u1a5diFV2ocCFQ2Oo90XYUFFck0of46ZPzbTwKCnc7HuF5Gw25FwQEARsBMb+00ShE947jqFmzZqk+ffrkXal37NihVq9enXflKowFwtQMTXIsk2TMjPv27at++OGHpCGiXaAtL4ymy0mDl+YIgqz3NGe90CUXr08OejzC5PPHH3/MO5yF5/OuSqVAgkCgCIhQFyic/iLbunWrateundaUYW7JXhoGo0i0fPly9fDDD2uzTsL2799f/fbbb5GCxn2GqRQmMddee63eE8P+M1ZT+XvnnXfCvmefzMSJE1WtWrV02piVcmQDe50Sof/973+qc+fOutzff/+9atq0qY4Xk7wPP/xQmX054DB8+HAd9o477lBdunTRbvCnTZumTfFOPvnkMNf4P//8s7rtttu0y/zzzjtPDRkyJGb2EPpIm3j4Yx8e9eEmNn0PGjRIm8NSduqAfWHpIISLF198UV188cW6bri++uqrEesdwefvf/+7xhLNK3w1cOBAnW+uuUbwIXijGaD+2cfjJvi/efPm2vnKCy+8oJ0UHXPMMRoreAGecFOsdkR8jzzyiLrlllvUZ599pm6++WbdRtijigMkcH3ooYe00OeO18tvu71TNtoU1u+YtEWiCRMm6DzQLu+++25F3t1kxwkfw9P2gsbGjRtVr169NE60+9GjR2ueIGybNm0U71NNfvoQJvd2ueH5Z555Rg0YMCC0zzCRevdaRhvPWHXkp216TZtwsfjTTzzZENZrn+xnPKJc8fpk4jvyyCP1GMleXPa4/+tf/1Ls/aQd0Y5HjRqVUoiE573Dm088773UElIQSA0CItSlBte4sbLi1qFDB4U3scWLF2vhio8+/fTTAt/ynknrY489prZv366mTJmiMMHgewY4v1SpUiUtIO3evVs1a9ZMx8dkir969eqFRff//t//004qXnrpJcVqK+k3btxYCw2JmLshcDEpQyjs16+feu6553S8b7/9tt5zZc4wwzsi+6MqVqyo9xgySOJhb968eRojsOC3KT+T1E8++UStXbtWO5sJK4Trx/z583U5ERY4TxAND5OAbt26hQnVaIl69+6thT0wp+yk2759ezV79mxXrMH+ZNLYokULLXDiFZW6mT59uhYohw0bFpYYYZ999lktJDApZU/WYYcdpicxPXr0UOx5yjWCD3fu3Km9uCLYRaKjjz5aT9zAhvqBl+FpfsML8IRN8doR8bVq1Upjx0SDBYiGDRuqJ598UvMiPEvcy5Yts6P1dA/PI8Cxz5PjTuAleP/zzz9XW7ZsKRAHHkIXLVqkhTD2jbE3Fm2k4Xc+oN4pM3sOqXN4GZ4mz5QVwgMubY68v/vuuxofHDIhJNO+4LFUn6nppw+hTbMvmUUZsP76669V0aJF1Zw5c0Jt02+9FwA3ygOvdeSnbUZJKuLjePwZ8aMsfui1T/YzHnnpk4mvU6dOejGyWrVqmofoH+Ap9t0y7tDuqO9UkfC8N2Tzjee9lVpCCQKpQ0CEutRhGzNmhDdMypiAMUnh77777lO333572HcIf3jExPkKTlDQsLGC3KRJEz0ZZPUxlYSQVK5cOT0xRGNA+vxmosWk2y8xkaxevbr+rHXr1qF4mXgyyUVgYYUXoQ5hpGrVqgqnJ2gzEGTRzIDVP//5T/Xyyy+rSEdHxHKXb/BEI1OjRg09gSc+9m6hObQH+smTJ+t9WghyYE7ZqQMm0uQzmlbVLyaRwoMtAgpaVPIH4UAHV/durRVCBpN3sEKY4x6+QsC76qqrdH1FSiMXnoE5f8mSqXcv7cgcKYKgT71TB0wUEZ6iCZjx8rd582a9cHDTTTfpOqJMCHho0XjnpgsuuEDvQSR98sHkFK0abQBC4Hn//ffVRRddpIVO6py6h6cfeOABraVFAKSdonmkLaGZR1iGn4iTcKeddpqe4LrTD/K3nz4E4e3ee+9VxYsX11mg/GjqKGeqzzb0Wkd+2qZXHP3wp9c4sylcrD7ZTz799Mlo2vFCDb/TPuB92gNjDXyVShKej49uvvN8fAQkhCAQPALiKCV4TOPGiDDAyiFaKHtwQWhyD35okRD+zj///LB4+a5UqVJ61R8hK1XEhMoQq6QIXGiOYu1xMuFjXfEidsIJJ4QFOffcc7XpHEKL24seuBgzUT7Cw1YixEZ8NIw4UwFvQ5itNWjQQJvy8YxJM5oxhDgmwDaRTyaADEqJ5sOOL9I99Y2myBCTeUws0SC5ecSEsa84VGCSL/QHAn7aEQKXzRtMCr1gHgtrBEU0PJgW/u1vfwvVDUJ3mTJlCnxq2oJ5YRZUzG8EC7R5TzzxRCgu3hEOU+aPPvpIC4H0EYbccVKuK6+8Umv58JabbBlNOu6rnz4E4Q1tPWaXRrCj/aFRTLUnW691lGzbdOPDbz/8Gen7wvDMb58MP5t2TD/t7sdTiZnwfHx0hefjYyQhBAG/CCS/BO43RQmv94aghTGTlliQYJrFJAchhwHK/DHxxCQTQSuVxECKySP7tTgvDOHnyy+/1FqkoNNl0MVldySBEc2FLQAnmjZxs1qL1sUmBn0OxjaEVoBB58EHHwxhbrBH20i9mP1/5pugrwiNmApx1AXaFszvEErdxESYsF988YXmB7SN7E8EM3c53d8Wlt+ZbkfwLntJ2ctDvTz11FPaPBb8E5lsorXDHNq9+EF8PENLB//GI3gejW4qtc5++hA0kmijb7zxRr2fEI04AiwYBaGxjYWHnzry2jZjpWe/yzR/2nnJ1vts6JO9YiM8Hx8p4fn4GEkIQcAvAiLU+UUswPBeJ9xMdOgAGSjcf+49cAFmT6eFsxLMD+vXr68noR988IHCbPL0008PMqm0xgXuXlf9hw4dWgBz6iDSnq0gC8E+J5xyMHkfO3as1hq+9tpr2hmMOx0m8TjdoK6oF4ROtHqYiaZK++LOQy78zlQ7MthwFiR7R9k3hgYN80faFkJVvhJtxU8fgnb5/vvv19YAOPvBTB2LBtobcaWavNSRn7bpJ7+Z5k8/ec1k2Ez1yV7LLDzvFSmlzcozMbfxnkMJKQjkFgIi1GWgvtD4MNlm4h2P0CpBZi9NvPBBvkegGDlypHYWwup5qs35OCwdcju4CLJM7MHD/JKVdjeh9TQ4o7ljn12k/U7u71LxG/M59hLilIV9T7EIhxpLlizRjgAwj8X7Id7eyL/QHwhksh3ZdUDbR3DAbJIjRTBDxnmDX4In2Cdk+NX+nmfwuZeFF/b+cKh7pL2pdpyJ3ifah2AaSp8DNlgqcHA9eU0HxasjP23Ta36zhT+95jcyZn1KAAAgAElEQVQT4TLdJ3sts/C8N6SE573hJKEEAT8IiFDnB62AwjKBYi8NXhht80mEGvar2YT7ZTRLOBBwE+79o7lDd4f1+hvHKzgMgYxDANzERyPMvBIxQ8T81C478eOVD5NUJr2pIjbJM2F042zSZ88ahNCNcwwwNsKmyRMCIcIu+5BSRewrxDQumiAN5rYnRPZq4TgGE0zM7myHL4nkkfKVLFky7NiIROLJ1DdgM27cuFDy6W5HoYT/72bNmjUFHNxgUlizZk29T9Wv+SN9AntMcRzh1mDRV6AJdLcj2pxNLFjQ5tz7S+0wyd776UPAgLy7y4OzCxy9sKIfj9z1Hi+8/d5rHfltm3Ya0e5TwZ/s4axbt64+gibVHk6jlSvZ5/Z4lOk+2WtZhOe9ISU87w0nCSUI+EFAhDo/aAUYFi91TCI4NwrhBkGB/SMIUJzJhMkdxD4PPDXimp6zswjLH/ecn+V2oGKyiMDBir1bcDLvuSLc4M2PPXJMhhBeuDcr/DgOQADF9In3xMUZbZz9g5aRZ5hHRdJ62elEukegZb8YAyACCk4fXn/9de2sBA2EISZ4lIU/92TPhHFfY5WdMmFKistrHKEYPLlHMGICaQhNARqRrl27au0ez9Hy4Y2U/YXJmjbGyifCJ5gY9/nUDWZsmL8isLG3zgim7NHC/JJJHPvuMOujDnGYMWPGDFMc31fqddKkSSn3OhgrY/AGe2ncAon7GzReCCngwjc4IrL3SHptR4bfDK/ZvAev0KbgWb+EoM25cNSnaZPEg/CMuSxaCIh37CslXZvID/VuhHW0SZhecybXW2+9pZ/zLf0C5xLiJda9V49+BSEOfOCV7t27qzPPPFPv2bTT8nsfq4789CGUGc0cZxSyXxAibo41QPvsFlJ5H6/e/ZTFax35aZt2+rHau1f+tOPzeg+PcYxGJilW2U2+4o1HhPPSJ5s2TJrcw0PmnjjoS+hP/S6kmHxyFZ6PPh7ZOMWq93zneRsHuRcE0oaAI5QxBNauXes0bNjQKVq0qFOrVi1n7ty5To8ePZxevXo5ixYtCsvX/Pnznfr16+uwpUqV0mF2794dFmbJkiVO2bJl2XhS4G/KlClhYc2PzZs3O02aNAnlYdmyZeaVvq5cuTIs3S5dujhbtmxx+vTp4xQvXtwZPny4c/DgwbBv4v0gL82aNXOmTp2qy01+Kf+cOXNCcZFGtWrVCpSD8lFON/kJT34nTZrk3HXXXSE8u3Xr5uzcudMdrQPG/fr1c8qUKaPzYuqpQECPD7zW0b59+5xBgwaF0q1Ro4bO86pVq5wqVao4tWvXdjZu3Ojs37/f6dmzpzNz5sywHBw4cEA/q1u3rrN169awd15/jBs3zmnVqpWzd+9er58EFm7o0KEF6h4+6dSpU8Q0qNMxY8ZovGgf/fv3d8DQTbHakc1D8Cft0bQn8mPyFI0H3WnZv5cuXeq0adPGefrpp53SpUvrspUvX163H5NPO33KSnoQ7cW0aXfatN+2bds6lLlYsWK6LdOv2AQPU57x48frfoOw/ME3u3btsoP6uqcuTL7sK2nZfZPXPoQ67N27t/P888+H2ma0MpmMeq13Ez7W1Usd8b3XtklYr+3d5CsWf5owfq7bt293GjRo4MyePdvPZ4GEdfOz4RE3D9uJxRuPCBuvTzbtlPRoO4ZPGU+oYzOuROtL7Py4701cpizmKjz/x3gEXoWZ5938Ir8FgXQjUIQE0yZBSkKCgFJau8BBsGi8WJ0VShwBVpxxtoFTCdzA24Q2pm3bturf//63732KaH3QyGKCyp9Q7iKAdo9D1XG8I3WZu/WYSM453BnHIvQP0tcmgqB8k2sICM/nWo1JfoNEQMwvg0RT4vKEAGZnmJgZMzRPH0mgiAhgwlK2bFnVp08f9eOPP2pzI9ZpcJiCKRvmo+7zACNGZD2kbjDV47BqPA8K5TYCmJlhNpqMuVluI1A4c4+pL2b8eCsWga5w8kBhK7XwfGGrcSlvAQTSrRqU9PIPgWgmKcY0xVwxUenQoUPIZCuWGU6uoOSn7LZJWpDl27NnjzNq1CinatWqIWwrV66szUZTlWaQ+c/FuLKh3r3g5jaFIt+RyB3OtNlI12im3JHizeSzXKmjTGJUmNMWng/fvlGYeUHKLgjkCwJifllAzJUHgoAgIAgIAoKAICAICAKCgCAgCOQOAmJ+mTt1JTkVBAQBQUAQEAQEAUFAEBAEBAFBoAACItQVgEQeCAKCgCAgCAgCgoAgIAgIAoKAIJA7CIhQlzt1JTkVBAQBQUAQEAQEAUFAEBAEBAFBoAACItQVgEQeCAKCgCAgCAgCgoAgIAgIAoKAIJA7CIhQF2BdcS5Y3bp1VZ06ddSmTZsCjFmiEgSSRyDT/Dly5EhVsmRJNWLECH30QvIlymwMgmfm8M8kL2W63lOBuuCZClTjx5lpXspkvcdHJ7UhMln2TNd7apGNHXthLntsZIJ5K0JdMDiGxUJnsWLFirBn8kMQyBYEMsmfv/76q5o0aZLas2dPtsCRdD4Ez6QhTCiCTPNSJus9IcDifCR4xgEoha8zyUuZrvcUwho36kyXPZP1HhecFAcozGVPJbSHpTLywhb3qaeeqt544w190OtRRx1V2Iov5Q0YgTVr1qhVq1ap9evXq6+++ko99dRT6sILL0w4lUzzZ+3atdVxxx2nxo8frw499NCEy5EtHwqewdXE3r171dy5c7XAX6VKFVWhQoWYkWeSl5Kpd1POpUuXqpNOOknVrFkzZjnT9TJX8UwXPqbepk+frm644QZVrly5QJJOhpeCyEAm691r/hkHFyxYoJYvX66aNWum51dev40VLpNlz3S9x8LFvMtXnjfly9eraOoCrlkm4CVKlFCXXnqpp5h///131bZtW3XdddfljXaPzmD48OF6YlakSBF18cUXq1dffVX99ttvYZjkY9nDCpjkjw0bNqiZM2fqAe27775TBw8eTDJGpQVEP/yZdIJWBPv371eU45577lFHHHGE9SZ3b/229yBLmk947tu3T/M6prkHDhyIC1Omy55ovZtyvvLKK+qXX36JW850BchVPNOFj6m3wYMHe7YyYLxj3GP8O+ecc1THjh0j1nmivBRE2TNd717KwDj43nvvqSVLlngJHgrDePm///1PzZo1K/TMvsl02TNZ7zYO0e4T4fnVq1erJ598Up188snq+OOPV7Vq1VKTJ08usN0i28seDZNceC5CXYC1xOrr6NGjVevWrT2vJh1++OHqtttuU82bN1dnnnlmzNzMmDFDsVKYzcSEjIEMLD777DPdmBHwhg0bplfa7Lz7Kbv9XVD32Y4n2opnn31W89PZZ5+ddLET4c+kE/2/CBgg0GJfffXVqmLFikFFm9F4BE9/8GNyO3DgwAKLO8SCBrdp06ZxNXSEzTQvJVPvlPOJJ57QCxv+0Etd6FzGM3WohMfshz/5EoGBcZDxGnPzxYsX64Ws5557TmHyZygZXjJxJHrNdL17zTfjYKNGjbwGV3PmzFHVqlXTY02NGjUUi8duynTZM1nvbiyi/fbL8+AOfz/yyCOKfXP4lbj99ttVkyZN1LRp00LJ5ELZQ5nNxRtHKGcQGDp0qDNlypSszu/WrVudOnXqOPPmzcvqfJK5XMCTfG7ZssWpW7eus2TJkqzHVDIoCERD4KeffnI6d+7s7N69O2IQnjdr1izr+7iImff5sFOnTrr/8fmZBM8gAn74c+bMmU65cuXCxsH169c7t956q/P+++9nsBS5mTTzHvqGaH2HXaq9e/fqMXP27NlO2bJlC0V/Ypc/yHuvPL9//37nH//4R4E+bc+ePc7jjz+u3xFGKPUIiKYuCyTxHTt2KNTW0chxHG1C0KdPn2hBfD1n5erHH3/09Y3XwJg8HHLIIerII4/09Em8snuKxGegoPH0mbwEFwTSikAq27vXgqCd6Nu3r/rhhx+8fiLhBIGcRABrlY8++khriuw90GeccYa6/vrr1ccffxxRW52Thc3CTGPaj/kfJq9gLpR6BBhjtm7dqrB+srfZMA885ZRT9LtIGtPU56zwpSBCXYbq/Oeff9Zml7h4P++889SQIUMi5mTq1KlaSLr22mv1nhP23rFPjb933nmnwDfsZxs0aJC6/PLLtU3zww8/rPcxmYDER0Pr37+/6tmzp97/969//Uvv27r77rt1Rzhq1CgT3POVvJAnGjD7YthHYPLZuXPnsHi8lJ0OALNNvr3jjjtUly5dtCknanw2qtNpu13j06m0a9dOl8HYc7OPAXW/Ib94mu/iXREUJ0yYoG6++WZdbsr/zDPPqAEDBoTtEULonThxorY1J4/8cQTGt99+Gy+JmO/j1XvMj7PsJXXu5h3qzTy74oorwurUa9kxCXnxxRdDvBlpryeb8bt166Zatmyp2ye8w96nDh06aJ7LteNKEmnvdjuinWEWaS867dq1S/cfmIxjXsZAThs1vAyGhhjgCYcpzwsvvKBNcI855hhdl5id0xdEIrst0S/ZcUYKn83PbDzpl+m3Ik1wvPAn/Aj/0xZMOzAYm/Zh97de+6Vsxo+82eWGn3C2ZMoLj5vxh2f8NgTfMAbCm2DPuGdPOk04r32ICc/VjGMmH2Y8pn1gblm6dOmwbRiEQ8ijLNu2bbOjyup7uz8Gd/CnLFxZpKEdm98GWz94eq0jGyS7vmP1I/Y3uXafSzx/9NFHq4ceekgvWrCtxhD9D4scZ511lhLngQaVFF9TrwyUFOIhMHjwYAdznFjkRQ2+b98+p1u3bk737t2dHTt2OAcOHHBmzZrl3Hnnnfpq4ietSpUqOQsWLHDWrVvnVKtWzXnmmWecnTt3OuPGjfNs5mDis69+TQWjlR1V/dy5c53PP//ceeihhzQ+kydPdl5//XVtgvH88887LVq0cH777TedPPg89thjztixYx1woOwzZszQ5YxksuoFT7tc8e7JG/nBxAYCf3Bu3rx5mMnI8OHDnRo1ajjLli1zDh48qPP56aefOtWrVw99604rHqZe690db7b+Bhf4sGnTps6vv/4ayibmNPA3Jh2GvJZ98+bNzr333uu8+eabofrAVLhx48bOgAEDTHQOJoITJ050Bg0a5FSoUMEhTUwGFy5c6CxatMi58sornenTp4fC58KNn/ZucKIP2bVrl25LtKkqVaro8lNe8J8wYYLuN9q3b+/06tVL8zvPu3bt6rRq1crBBMpN8UyoTJukbffp0ycUJ/FjwmPXuzvubP0Nhi1bttQYUT7+6AOuueaaMFMlg3s8/qSc27Zt030ifY6h33//3enYsaMzf/5880hfvfZLYR9l6Q/a31133aXHLPqIIUOGOPAK99CwYcPCzB0JX69ePT32MR7QJ8PXbdq0CeMlr32I4U97PFm8eLHz9NNPO/QlhlavXu1UrFgxrH7NO77FJDCXTOnBhz6wb9++uhgbNmzQuFJ2aOPGjZq/CQd5xZOwXuvI3XdQ5126dHHGjx+vx1CdsOsf4yZzG7u+XEGy/meu8Hw0IDdt2uTUrl07bP4ZLaw8DwYB0dSlWGj2En1Q7t3xMoSnqMcee0yvTGIGiWMK3ADjqMReHUajhIdOVliOPfZYVa9ePVW0aFGtNWRVM10Urew8ZyW6atWqodVNVkBZHSTP//znP9XLL78cWv3ZvHmz1kDedNNN6rDDDtPazfLly6tevXop3qWa2CR87733quLFi+ukwBBN3UUXXRTmLW3t2rXaHTbaWVY3qSPcY4P9zp07E8qmn3pPKIE0fwQu11xzjU4V81xDmAzDD7Zpr9eyg+3u3bu1l1n4B8KlPO7z8cRlCE0zz3Dmgnbp008/VY0bN9ZtBc0e6aE1zzXy0t5ZVX3//fc1z+LBjH6BtoSzgQceeEA7OWEFHvxxXoB5E/z9+OOP6/6G53g7o56S8ex4wQUXaE0pbYg4cXqwceNGRfvPNYJ/wKJhw4a634L37rvvPu1AwC6LV/7kmxNPPFFjYmsvwaZYsWJaO2TH67Vfsr/J1ntW+0877TRtykUfgSaM/pSyM7bxB+9AmPviYRTeZAykn4WfcNqwZcsWbZliyum1DzHhzRVHEGPHjlXt27fXfYl5jkMg0o/m3Or777/PKU0dfQBjKe0arQv9Jv3kunXrdJHR1tFfEw7yiqefOjLYcqWfwpqIPprxgLrNV8oVno+EP/X0wQcf6PkNczmh9CCQv60hPfhlTSo0IEyhGMDsSS8ZPPfcc7VgQydqCKGJgRHCBt39jQmXLVfya0xPTZ7N5JzfDDSYL2G2hecxQ1dddVWBCZR5F+SVyS1mVQjVhsC0RYsW6oQTTjCPtKCHSSjlIZ9MDHC5nOgk2G+9hzKS5TeY/WGavGjRIp1ThAnO7DOTNh76Kfv555+v3VtfcsklOj4mgphI2RNjNySYSNGezL4M2gvCdy5OIry0dwQLzKvwWGYmaGBCuTF5BiuEK5voW0w/YsImi08q4rTznK57hIwpU6boyae9UAZe1IdNfvkTvpw/f37IkyJ1w0KR+6gQr/2SnZdsvaftsZBgTIGXLVumvvnmG70og6AG32HaCyFo0KeCq03UQ6lSpULHB/npQ+x4GGvYI1q/fn3dJ9jv8vGeNkmZ6TfBFdNAjtsBvxUrVoQ8d/vB02sd2XiyfWHMmDH6eJ/KlSuH9T12uHy5z2We52xdFlLxBm+PJ/lSN9laDhHqsrVmfOaL1UE6yQcffFB3dEwczB8TWQY9OsRcJSbz9sTIXQ7eYfvPCh5hOaibA0uhdAisaBPQ8Nx44416Xx2aUSbJpG1Pchn05s2bp/7+97+ryy67TO9X+vLLL3Xn5y6Tl9/5Wu/w7i233KJXfVkdRpvGAGcLyH7LzqJGv379tJaJuJmUrVy5MirMCJZMAAsLIbChGWUV3k08Q7CmjxHyhgB8y6TGaO/jfeWHPxFuWNSCf+lTOP+R/sRNXvsl93fZ+hth1jhjMP07AsbChQvD2irjHYts8K0ZB7nSF7O30yz8+e1DwIV+HQsQBPZI/Qd7h9ByG02WG0uEbxYhc4lOP/10jR0HgfMHBiy4ob2jb+YwbcgPnl7ryODE/IXjId59912NPWkVBsoFnnfXAwIdi07sTReBzo1Oan+LUJdafNMe+9ChQ/Ugz0Bv/33yySfa0UPaM5TGBDF3efvttxUdCpNxTMYwRcVRQaqJjuv+++9Xc+fO1c5aMLvCPATcqQeIK85fyBMrvExKME9gJYtBMxnKx3rHqQCmswgSTFrLli0bESIvZUcjykZuBBdMptBqv/baa3qTf8RI5aEgEBACmEvGI7/8yWIRZsBffPGF2r59e1Th0Uu/FC9v2fQe7T19Ato6+lO2DdCW3UIdeUagRXCwx0Fzz3c2eelDTPi33npLm3VidolHajRYNlHfJUqUiGj2T1+GQMcCVS4RpuiMr+COdhSTdDR2OKWhLEZDasrkFU8/dYRHUYRJnI9xzhxjZ2GgXOB5ux5YpIZH2CojAp2NTHruRahLD84pTwWzG/ZUpGP/WMoLk0QCrMYy+HDAL2aN2KQPHjw4iRj9fcrqORo70mSl+M0339T7PogFgWLkyJGqd+/eOkwQHV4+1zuTH/bNMHFwm16Cp5+yMyFASGQixr4coYIIgAsaBkys3MQzNBDJLj64483n3/RFmFlG09jYZU+EP9mLi5YKzVUk00s7/lj9kh0u2+8RlsD0s88+06aVHBGAOR7aI6MtogxGaIrEy3YZ/fQh5rtHH31UtwNMkjHvZCERrawhBCCsY6gb4w2SdwiUmMnihRMrgFwieJl9deBOX4zpI8IGB6xj4mvID55e68jEXb16dXXnnXfqumVbA8J1LPN5812uX3OB5w3GCHQsbLs1dCxaYekhlHoERKhLPcYpSwFND5uuIQa6SpUqKVz+u90lY9aDMGEPPCnLVIYiZlC3HV6QDVaza9asqV1P205iomXRxjNamEjPiRuHBAzaNuGIhg3CrBZDrDJijuVe1bS/oePzYyabqnqHXxi0EUzd5bLzm+p7NsJz3AcChW16Sbp+yg5vMNmKJkiDeSoHnWzBM1Z9oWFg3yqODtx1Dn+j/Y7m/CFWvNHegfe4ceOivQ7kOVqUunXr6mNDmFikk+BZ9vRihmTM/Uif/pmJj02J8KdxooB790iml177JTsf8e4ziSd5MxojtOwIGZi2mv23dr+KeSr8DN+6iTESgQvy04eYePgGoi/B8crnn3+uBWv7Pc6FZs+eHSZ0sKiHFQnOvBLZEpDpPoTxAJNTiL7A7G+2TdT94Om1jmxcES6hMmXK6L2/CJW24GzCBnkVnv+jnYBpNJ7nHXvUjWMxe5xl3slebS9zMHe9ZZrn3fnJhd8i1GVBLcH0rCjaA787W6y0orVgJYTJEBMD7u2VczRErLZ37do15LADxx14AWPQp8NlskZ6/HHPZNbckyZCB3En0gD5nviwdSceLxSv7O78RosTs5Y2bdooNs8bHBGi6BRYNWUF0SYveNrhY91TBjRzTK6Mt0Zw+Prrr7W5ipkIo3lioodJJnVIPjEr5LxAVvN5RqdoO7Qh3XiYeqn3WPmP9o58sIchk3sX2KDPSiX7CiKR17KjyWBggT8geBxTWEx4mBSwN8aeaBu+4xoUZQJPUw7TxmO1dyZMmKWhGWUVHFzgUVbnBw4cqM1pzGSU55hAEa9NpMd3kSZaCCFYEoA1+WCCyCTdUCJxmm/jXekHcOiQbmJyz4LT6NGjNZbwAPttEUAwI8MUGPLLn3xDf46mij4/0r49r/1SIphkCk94FE0XptgIE2jnWDxDa2kTe6wfeeQR1aNHD82/8JbhZUzDbAcqXvsQ05bssRHBpFGjRqp79+7a6ZXJA8J87dq1tYdmhDnq/Y033tCCEJ5oE6VM9CEmrziMwtoBz7fwHpiDva0hJaxXPP3UEbxMf0MdQvAB/gMY99HURuqnWSgBe4RrxtZkSXj+DwQj8Txzrf/+9796ywvzBc5w5A+BmPqh77UXXfzURSZ53k8+syZsMCcjSCx+ETBnqGCVYf/FOsOGs4yaNGniFC1a1KlVq5Y+68ydLmfp9OvXzylTpoyOl3Cc92Zo6NChofQ4v4UzrEif81yWLl2qr/zmuR/i3B3ybpeF+2bNmoXOBTPxeSm7lzAmPq7knfOHODOodOnSOh/ly5fXZ0KZ83Ps8Nx7wdP9TaTfnJnTu3dvfWYS5yhRP8WKFdN1tXbt2rBPVq5c6dSvX1+HKVWqlD5rh7JyLlfx4sV1fs25S6ZuvGAar97DMuHxB2fFRTtzzGMUSQfjvELO7tq+fXvUuLyUHR7g7DnTLjgrcNKkSc6qVav0GWycpcN5S3b7sHH32x4iZTYTeNrl8dreaRdt27Z14M9IfOxum6QBEb/BLFI/Bl+PGTNG1wFx9+/fX59pxbeJxhkJZ/czeKdBgwb6zEH3u3T8pg9o2LBhqN+mP+7Ro4c+24tzqCCv/OnO77x585yPP/7Y/Vj/9tMvRYwgysNM48lY85///CeUO8Y7zoaLRJzbZ/e3nHlIf+GmeH1INP60xz03z3OuIjzOeAS/c6YeZ8EmQ5noQ0x+4SfGKYP18uXLw874NOG4xsPTDhuvjuw+jHkKdQHZ46PdP9v9kOmPuNph7PS93AvPq9C5i5F43n5mY27ue/bs6QXmiGEyyfMRM5TlD4uQv6yRMCUjgoAgkBUIsCKKBhGTXv4yRWiaOabC7dggU/lJNN1swTPR/Ofyd5gF4bihXbt22mNkLpfFnff33ntPa7FtEzh3mKB/5zOeQWMVZHzShwSJpr+4hOf94RVUaOF5/0j+cVqk/+/kC0FAEMhTBDBzwdwO73p48Ew3YW6ByQaOCDCJzMXDvm3MMo2nnZfCdo+5LSaOeJjF5DofiH1imJaxXxdTJ8xa00X5iGe6sEsmHelDkkEvuW+F55PDL9GvhecTQ040dYnhlvdfsZH8vvvu02eqxSsse2Myqc2Jlz+/7wtz2f1ilYrw7C9s2LCh3n/FXpVbb71V76FIRVoSpyCQawhw1mLHjh11/8wVr8dCgoAgIAgIAoKACHXCA4KAICAICAKCgCAgCAgCgoAgIAjkMALi/TKHK0+yLggIAoKAICAICAKCgCAgCAgCgoAIdcIDgoAgIAgIAoKAICAICAKCgCAgCOQwAiLU5XDlSdYFAUFAEBAEBAFBQBAQBAQBQUAQEKFOeEAQEAQEAUFAEBAEBAFBQBAQBASBHEZAhLoAK++nn35SdevWVXXq1FGbNm0KMObsjyrTZR85cqQqWbKkGjFihCpsRy9msuyZrvdMtoxMlz2T9Z5J3Enba9mnTp2qPacWKVIkdO3cuXOmsy/pCwKCgCAgCAgCgSMgQl3gkP4x4VixYkUKYs7+KJlsZarsnG82adIkfXZT9iMVbA4zXfZM1nuwSPqPLZNlz3S9+0cruC+8lJ2jVljkMX9LlixRxx13XHCZkJgEAUFAEBAEBIEsQUAOHw+wIk499VT1xhtv6ENujzrqqABjDi6qvXv36gOdp0+frg93LleuXCCRZ7rstWvX1pO18ePHq0MPPTSQMgUdyZo1a9SCBQvU8uXLVbNmzQI7DDmTZc90vXupI+F5LyilJkw+8nxqkJJYBQFBQBAQBASB5BAQTV1y+BX4ev369apEiRLq0ksvLfAuGx7s27dPzZw5Uw0ePNiTRosV7m+++UablB5//PHqnHPOUe3atVNbt24tUJxMln3//v3qu+++U/fcc4864ogjCuQtGx5s2LBBvffeewptQSI0e/Zs9fjjj6vffvst7PNMlz2T9R4GRJQffnn+559/VrfddlvIXM823eOwZ9u8N5Nlz3S9R4E77HEiPI8GjgO2K1SooOvg7rvv1gshdsS5UHY7v3IvCAgCgoAgIAikGgER6gJEeOnSpWr06NGqdevWgWlhAsyejgrTo6ZNmz9tRWcAABm+SURBVOoJk5e4p02bpt588031yiuvqB07dqj58+erww47TLVq1Urt3LkzFEUmy86kHQ3p1VdfrSpWrBjKU7bdMElt1KhRQtnatm2beumll9Tvv/8e9n2my57Jeg8DIsYPvzxPVNWrV1dbtmwp8Pfss89qQYMwmSx7pus9Btxhr/zyPAIdGB88eFB99tln6sCBA+ovf/mLat++fai/yZWyhwEhPwQBQUAQEAQEgRQjIOaXAQJcunRp1bZt2wBjzHxUEyZMUCeccII6/fTT9WQWbR2mg/Xr19cav1tuuUVnMpNlP/zww1XLli0zD1aKcoBm6L///a+aMmWKqlmzZlgqmS57Jus9DIgAf+zatUudd9556uSTT44ZaybLnul6jwlMgi/h8wEDBqgzzzxTNW/ePGRGDc4IeZQZyseyJwiZfCYICAKCgCAgCIQQEE1dCAq5iYQAK+2YM7I6bujEE09U559/fgEzQPNersEiMGfOHLVw4cK8WzAIFqXgYtuzZ4/CvE8ovQhs3LhROzpCS2rvi33wwQdVly5dstb6Ib0oSWqCgCAgCAgCgkBkBESoi4xLyp5isnXFFVdorRer0Tj2MHt2cL/9zjvvhP02GcG5xsMPP6zQlF1++eWqf//+EYUqnEIMGjRIhyEs37DXLBa59xCRB0No4nABfvTRR5tHivAc2cAKei4R5TBYgzv485vrDz/8ENpHxW+zb80Pnl7ryMbMrm/2cYGtTZhdDhw4UJu7IkznIuUaz4MxJsb5QLnE87Sf7du3q1NOOSUfoJcyCAKCgCAgCAgC6UXAEUo7AosWLXLuuusuZ926dc7BgwedIUOGOM8//7y+JzPDhg1z5s2bF8oX4evVq+fMmjXLOXDggLNjxw6ne/fuTps2bZw9e/aEwu3bt8/p1q2bfkcYwvLNnXfeqa8m4O7du51mzZo5U6ZMMY+cxYsXO08//bSzdevW0DP3DfGtXLnSefLJJ53Ro0eH8usOl62/wadz585O3759dRY3bNigcaXs0MaNG51evXo5hIO84klYr3UE5mBPHUDUf5cuXZzx48fr+tIP/+8f71599VXn/fff10+GDh0a9q0dNtvvc4nnZ8+erdvG8OHDnfLlyztKKadq1arO1KlThectRgua5wcPHuxUq1ZN94u9e/d2Spcu7RQtWtSpX7++s2zZMivlxG+XLFni9OzZM/EI5EtBQBAQBAQBQSBLERBNXXplaJ3aWWedpU477TTtQRJNERqvtWvXKvby4AiDvwsuuECHxXEATkrweogjkEMOOURr65o0aaKdOOAi39DkyZMV3uYee+wxHYawfMMeuGHDhhVwsmG+Q+s2duxY7YzgpJNOMo/DrmhbrrrqKnXTTTdpTePtt9+utVxhgbL8B9qX8uXLqx9//FE7YKCsaAXWrVunc4627pprrglpabzi6aeObIjYQzRq1Cjt3KVq1aq6bu33mF1iBog5Wq5TLvE85pdoUNlTxz5GnHU899xzqkWLFoo6ySXKJZ7n+AP6xSFDhqiLL75YH/9B33TdddepNm3aKMwzhQQBQUAQEAQEAUEgMgIi1EXGJaVPixYtqo8GWL16tU5n2bJl+tgA3KPjcQ9h7JhjjtHvEDR++eUXvYfNzhSmlaVKlQod9I2AwNlzCHFHHnmkHVSde+65avPmzQrhw00//fST6tu3r3Z8Qr6iEYLn3Llz9aSWbzDrZMKVawQW5B8BGlwRVjniAfw4NB0nDZAfPL3WkY0Vjh/GjBmjHUBUrly5gICM2SXv2U+UD6aAucTzLKjgIRbTY7CnPSJ04xwo1uKIXb/ZdJ8rPA9mM2bMUH/961/VrbfeqrHH7PuBBx5QxYoVU1988UU2wSp5EQQEAUFAEBAEsgoBEeoyVB0IX0xg2LtltG0IGDjEQFgzhJA3YsQIrVEy+8G4MtF84YUXQg4d0C4gXCAE2OG4v+SSS7SwiCBhE0cS9OrVS2sjVq5cab+KeE9caC/QBOINE20Gwk8uEfkGO7QC/IHBokWLtPYOoZrDtCE/eHqtI4MT9TBp0iT17rvvauxJyyYwff/993VdmvzY73P1Pld4Hh5h36tN8D75h1dYDMglygWeN3iywHHttdean/p67LHH6iNY2HOXa/1NWEHkhyAgCAgCgoAgkEIERKhLIbixoi5ZsqTWnqGtY6JSr149rWlzC3XEUa1aNS2UEc79x3c2DR06tEAYvvnkk08KuGh/6623tFknZ0D16dNHa7DsuKLdowlk4sVxBzg2yCXizLKzzz5bgTuHqjdu3FhP0nFSgzbJaEhNmbzi6aeOPvroIy1M4r4dr6IffPCBSU5fOdgdgY8D7G0BHYGd8/jII0IHiwC5RLnC85g/o8l1k+2R0f0um3/nAs+DH4tZmEIbJ0U2ppQBDTuOi4QEAUFAEBAEBAFBoCACItQVxCQtT0qUKKHddnPALscDXH/99drcDu2RrZ0xJpGRJpl2Rjl2ABMlzCy90qOPPqo1bjfccIPOw9tvv633D5nvEQZHjhypD72ONJliv5db+2e+zdYrQhL76sB91apVCs0Awsarr76qLrroolC2/eDptY5M5OyRu/POO7UQyT4thGu0EIbQhiKEuwV4BEz2R+7evVt9++23Oed9NBd4HoGORY6GDRuqHTt2mCrRV3CnrnPNHDYXeB6A6QfNge9hwCul9xyfccYZ6qijjnK/kt+CgCAgCAgCgoAgoJQSoS5DbGBWz1977TUtZBQvXlxr0hAwbG3ROeeco3BlH8lBw7Rp00LaGrQIlSpVUjxjP5ZN7KVDOMPhg01G88AkFccrn3/+uTYJNWEwC2Qfy/fffx8mvCHMzZ8/X5UtW1ZPck14r1fyQjkxK0VwSTeRNg4wILQDximNbfbqB0+vdWTKSdxMtKEyZcoonM4gVEbSUJhvgrii6ahbt66qU6dORvZD5gLP00YQ5nAIxL5VQzz/6quvNM/bz837eFfh+fg8j2aatmkWNAym9F/sd8VRUyop03WUyrJJ3IKAICAICAL5j4AIdRmqYyb1nDeHYIQwgXaOSU25cuXCcsQE8pFHHlE9evTQ2iUEKv7QNGE+yOq2oSpVqmjvcV27dtVeMHmON0y8Z1522WWhA30RpJikopUwhGDSqFEj1b1799CEn1VxtHiYCBozSyZYaJa+/PJL1aBBA30wuYnDz5V42Ffm3k/mJ45Ew7Lif+GFF+p9OghYYA72toaUuL3i6aeOwB08qUMIPsCskv2QOEaJJuTynP1/hHOfZecXByavTJLTTbnA8zjmuP/++/XeObBGE42Wevjw4Vqou++++0ICuV/8hOdj8zzeaOnr+vXrpxehaCMsdOCchnqhPaaaMllHqS6bxC8ICAKCgCCQ5whk6VELhSJbnJn0n//8J1TWfv36OatXrw79tm/mz5+vz2vi3KZSpUrp89TMWWd2OJ4RT5kyZfT5WrVq1XLmzp0bCrJlyxZ9FhQOHvnj7DOIvJQtW1Y/48pviLPpOEONc7oIT9qtWrVy1q5dq98n+m/cuHE6nr179yYaRcLfcf5bnz59QlgvX77cGTBgQMT44uFpfxSvjsDa4M55XNQF1KlTp9Bz7t1kf2e+t8+6c4eP9Xv79u1OgwYNHM5iywTlAs/DH3PmzHFq166tz0krVqyY06RJE2fVqlVJQSY8H5/nwX7SpEn6HE/6Os6q4+zIXbt2JYW9+Rj+i3VOXSbryORRroKAICAICAKCQCIIFOGjPJdbpXhZhgAr8D179tTmopiMCqUPgcWLFyv25rVr105rP9KXcuFOSXg+O+of50Ljxo1TrVq1KpAhqaMCkMgDQUAQEAQEgRxCQMwvc6iy8iGrmB7i9RHvmRUrVsyHIuVMGTgPkUPmW7duLQJdGmtNeD6NYLuSwqstZr/mj0PNIzmdkjpyASc/BQFBQBAQBHIOAdHU5VyVSYYFAUFAEBAEBAFBQBAQBAQBQUAQ+BMB0dT9iYXcCQKCgCAgCAgCgoAgIAgIAoKAIJBzCIhQl3NVJhkWBAQBQUAQEAQEAUFAEBAEBAFB4E8ERKj7Ewu5EwQEAUFAEBAEBAFBQBAQBAQBQSDnEBChLueqTDIsCAgCgoAgIAgIAoKAICAICAKCwJ8IiFD3JxZyJwgIAoKAICAICAKCgCAgCAgCgkDOISBCXc5VWXozPHLkSFWyZEk1YsQIJUcaRsb+p59+UnXr1lV16tRRmzZtihxIKfXDDz+ogQMHKs7DEhIEBAFBQBAQBAQBQUAQEASCQkCEuqCQzON4fv31VzVp0iS1Z8+ePC5l8kVDAF6xYkXUiM466yx1wQUXqD59+ohgFxUleSEICAKCgCAgCAgCgoAg4BeBw/x+IOGV2rt3r5o7d65aunSpOumkk1TNmjXzFpbatWur4447To0fP14deuihSZdz8eLF6vvvv1fLly9XzZo1y4tDsE899VT1xhtv6LIcddRRMTG64YYb1IIFC9SXX36pbrrppphh5aUgIAgIAoKAICAICAKCgCDgBQHR1HlByRVm3759aubMmeqVV15Rv/zyi+ttfv3EVPC7775T99xzjzriiCOSLhwC3YABA9SSJUsSigtN2HXXXafatm2rfv/994TiSMVH69evVyVKlFCXXnppzOiLFCmiEJRHjRqlfvzxx5hh5aUgIAgIAoKAICAICAKCgCDgBQER6ryg5AqD5uqJJ57Qgo7rVejnjBkz1PTp00O/c/EG4RUN1NVXX60qVqwYSBGqV6+u7r333oTjOvPMM1Xz5s3Vbbfdpg4//PCE4/HzIWan7IX77bffIn6Gxnb06NGqdevWnjSPp512mipXrpwW7GSfYkRI5aEgIAgIAoKAICAICAKCgA8ExPzSB1h+gq5cuVKdc845fj7JurAITS1btsyqfB199NHqwQcfTGuedu3apTZs2BA1zdKlS2vNYdQAEV5geom2cePGjap48eIRQsgjQUAQEAQEAUFAEBAEBAFBwBsCoqnzhpPnUGheZs2apZ1heP5IAnpGAHx//vlnhbljOggnMX379tWeK4NM74wzzlAnnHCCWrRoUZDRSlyCgCAgCAgCgoAgIAgIAoUQARHqPFb61q1bVbt27bT27fLLL9cu/t17uqZOnaoOOeQQde211+o9d+z9Yg8Vf++8804oJcz1rrjiCv2cK78x7cOs0ITv3Lmz1uL06tVLPyduTPxI++STT1Zt2rTR70OR/t8NTlwGDRqkwx1//PHq4Ycf1nviTDgEIkwXTTqRriZP5huvVwSuCRMmqJtvvlnHX6FCBTV8+HDtWCZSHAhM7EtEo0m53nrrraheIQ1m4HvKKaeoiRMnFogyXtntD1avXq2aNm2qsQRPtGbUsSFTH5javvDCC9oM9ZhjjtHlAj9wTIbQOHJUxPz585OJRr4VBAQBQUAQEAQEAUFAEBAElAh1HpgA4aNDhw4KL4d4b8RJCvTpp5+GfV2pUiV9ltvu3bu1Z8cpU6bo3wg79erVC4XFXI8jAh566CH16quvKn4zycfVfceOHfVEv3379urYY49V5513nvr222/Vu+++q4iXtNFSXXLJJapFixZh56Lh1KR3795aOCHt7du3q8cee0wR1+zZs3X6CDCffPJJKF/kzf1HeuTJL5HH7t27q5deekkdPHhQjRkzRgtfCKbus9l27Nih83r77bdrb5gfffSR+vDDD/U+s0jpkh/yRTz/+Mc/CgTxUnbzEYIU9XHHHXdo/Dg/7sgjj1TdunULOV+hPvr376+xAUs8dYI/WIEfOCZLCHXgQJxCgoAgIAgIAoKAICAICAKCQKIIiFDnATmEN7xcNmzYUAtfTPjvu+8+hUCSKJ144omqWrVq2rW/iYO9W8WKFQsJVEWLFlW33HKL1uqh/UMQIW0EkAceeEDhcOPzzz83n6vJkyfrvV8Icmjp0Grh5ASBZNiwYSGBJfRBgDd4ckSoQzC66qqrtEaLvWLPP/+8+uqrr7RAZic3b9487QUSYY18IuCwV27cuHExPYpyrAIYuMlr2Y128JFHHlE1atRQhx12mI4P3PHMGc0Ziju9oH6zp07O/wsKTYlHEBAEBAFBQBAQBASBwomACHVx6h0TSzQ1eH9EUDKE2WKy57YhcKE1QtCAOLsNzVykowPOPfdcLSiZ9BFsrrzySrVw4UJ14MABre3B2yZxIvTZxLebN28OpWO/C+p+zpw56vTTT1cXXnhhWJS4+ccME8HOJrSaHMRtE8cBkM9NmzbZj+Peo+nyWnYc2OD05Prrrw/DE1NMzD/Z55ZOYm9dvLPt0pkfSUsQEAQEAUFAEBAEBAFBIPcQEKEuTp0hMGF2lwoPhewlQzhD0EAw4Ty4yy67LE6O/nzNfi/2gSF4ou3BjBBtl3ufHKaaW7Zs0SaRqdpTh+aN4wbcWjTyglBp9g2a3CMQ884mfoMDmPshr2UnTjSuaEDRlNqEIA2e6aS1a9dqIdKNQzrzIGkJAoKAICAICAKCgCAgCOQ+AnKkgcc6dAsBHj+LGQyNGmaVX3zxhTr77LMDER6HDh0atn8vUgbYE5aP5KXslJu6TNcZd9FwxsxzzZo1Mc86jPatPBcEBAFBQBAQBAQBQUAQEARsBERTZ6MR4R4tClqldevWRXib/CMOoUaLxWHl0Uwvo6WCpseY76FpYl8Y5ouZoIsuukht27Yt4r498lSqVKkCWjx3PvFeCd5+zRH9lJ24Mb80Jq92HnC2wr7GdJAxh/WjmU1HviQNQUAQEAQEAUFAEBAEBIHcQ0CEujh1hhCA4w/2vtkeHBFg5s6dG+frP18vWLBAO+L488kfd2eddZZ2eMKRB7Em+JiA2oRQgCdMszcMwZN9atOmTdPClR0WAWbkyJG+zRrtOOLdo3FctWqVwmGKTeBEPqtWrWo/1lpJG09erlixQjuJQVD1Q37KjuCMiWikuiOf7GuMRwifOHRJhtiDyJ5Iv2VNJk35VhAQBAQBQUAQEAQEAUEgPxEQoc5DveIlEVM5zolDEEFIwpsk55YNGDBAjR07NiwWhAa8On755Zf6jDYEG+5xJOImBBIEMzxZxtq3RzoIHRwV8NNPP+mjA9jDhhMSQ1WqVNHxdO3aVWujeI5WirPgEBhJK1XE/sDatWvr8/MQjNgbR9rkBeEFwdgm9hGCJ2aIlAlNJUc61KlTp4CjF/u7aPdey37SSSep+vXrq06dOmnnKtQnfzha4Yw9nLW4CcEbIZo8k1cc5ySz/w5+GD9+vLr77rsL7Ct0py2/BQFBQBAQBAQBQUAQEAQEgXgIiFAXDyGltDDG+WvsRcPEkfPl0IohqNx1110FPD4SJQ5L0Fxxtl2jRo300QScOxeJEPZuvfXWiF4vTfgmTZqETDSvueYahVdJzoOz4+T+5ZdfVueff74+LgFTxpYtW2rX/ThLSSWRVt26dRVHBfDHMQW1atVSmJe2bt1aHx1g0kcg4oB0PGVyVhx73Hr27KmFT3CNRThRQRDE2YlNfsqOkN6lSxeNH/WJF068cz755JMRBUqOW0AQ5BgJNH1oFPGGmggh7CLMVq5cWR+6nkgc8o0gIAgIAoKAICAICAKCgCBgI1DEYZYplFEE3nvvPX0UAfvO3IQA06pVq5Ag6X6f77/RiuJQhvPkILxXtmnTRgurl19+ec4Vn/P0vvnmG30ovClTzhVCMiwICAKCgCAgCAgCgoAgkFUIiKYuQ9XBnqoPP/xQO2DBJT8mfpGI4wpw3sG1sBFmihzyPnDgQG32iIA7YsQIrYmMZCaZ7fhw5MSyZctUixYtQkJqtudZ8icICAKCgCAgCAgCgoAgkP0IiFCXoTr6+uuvVePGjfXeuL/97W8R97vhFRMzPZyo3HTTTapz584Zym1mksUsE4xee+01jc+NN96o97I98cQTOSkUIbhjRus+HD4z6EqqgoAgIAgIAoKAICAICAL5goCYX+ZLTUo5BAFBQBAQBAQBQUAQEAQEAUGgUCIgmrpCWe1SaEFAEBAEBAFBQBAQBAQBQUAQyBcERKjLl5qUcggCgoAgIAgIAoKAICAICAKCQKFEQIS6QlntUmhBQBAQBAQBQUAQEAQEAUFAEMgXBESoy5ealHIIAoKAICAICAKCgCAgCAgCgkChRECEukJZ7VJoQUAQEAQEAUFAEBAEBAFBQBDIFwREqMuXmpRyCAKCgCAgCAgCgoAgIAgIAoJAoURAhLpCWe1SaEFAEBAEBAFBQBAQBAQBQUAQyBcERKjLl5qUcggCgoAgIAgIAoKAICAICAKCQKFEQIS6QlntUmhBQBAQBAQBQUAQEAQEAUFAEMgXBESoy5ealHIIAoKAICAICAKCgCAgCAgCgkChRECEukJZ7VJoQUAQEAQEAUFAEBAEBAFBQBDIFwREqMuXmpRyCAKCgCAgCAgCgoAgIAgIAoJAoURAhLpCWe1SaEFAEBAEBAFBQBAQBAQBQUAQyBcERKjLl5qUcggCgoAgIAgIAoKAICAICAKCQKFEQIS6QlntUmhBQBAQBAQBQUAQEAQEAUFAEMgXBESoy5ealHIIAoKAICAICAKCgCAgCAgCgkChRECEukJZ7VJoQUAQEAQEAUFAEBAEBAFBQBDIFwREqMuXmpRyCAKCgCAgCAgCgoAgIAgIAoJAoUTgsFGjRhXKgkuhBQFBQBAQBAQBQUAQEAQEAUFAEMgHBERTlw+1KGUQBAQBQUAQEAQEAUFAEBAEBIFCi0ARx3GcQlt6KbggIAgIAoKAICAICAKCgCAgCAgCOY6AaOpyvAIl+4KAICAICAKCgCAgCAgCgoAgULgR+P9b3MnyFP8gNwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>5233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    shop_id  item_id\n",
       "ID                  \n",
       "0         5     5037\n",
       "1         5     5320\n",
       "2         5     5233\n",
       "3         5     5232\n",
       "4         5     5268"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对于每个月，我们从该月的所有商店/产品的而唯一标识符组合创建一个网格\n",
    "matrix = []#空列表\n",
    "cols = ['date_block_num','shop_id','item_id']#训练集里面的特征\n",
    "for i in train['date_block_num'].unique():#找出每一个,只相当于对每一个进行后续操作,减少运算的意思\n",
    "    sales = train[train.date_block_num==i]#取出来相当于前提条件\n",
    "    matrix.append(np.array(list(product([i], sales.shop_id.unique(), sales.item_id.unique())), dtype='int16'))#进行添加到列表里面\n",
    "matrix = pd.DataFrame(np.vstack(matrix), columns=cols)#做成df的格式\n",
    "matrix['date_block_num'] = matrix['date_block_num'].astype(np.int8)#转换数据类型\n",
    "matrix['shop_id'] = matrix['shop_id'].astype(np.int8)\n",
    "matrix['item_id'] = matrix['item_id'].astype(np.int16)\n",
    "matrix.sort_values(cols,inplace=True)#进行排序\n",
    "\n",
    "#求每月该商品的售出量:item_cnt_month\n",
    "group = train.groupby(['date_block_num','shop_id','item_id']).agg({'item_cnt_day': ['sum']})##groupby的作用，相当于一个月卖出的东西\n",
    "group.columns = ['item_cnt_month']\n",
    "group.reset_index(inplace=True)#重置索引\n",
    "\n",
    "matrix = pd.merge(matrix, group, on=cols, how='left')#进行组合\n",
    "##指定这一列的值#做好了标签列\n",
    "matrix['item_cnt_month'] = (matrix['item_cnt_month'].fillna(0).clip(0,20).astype(np.float16))#把这一列空值进行填充和进行范围限制\n",
    "\n",
    "#要使用时间技巧，将测试集和matrix进行组合\n",
    "test['date_block_num'] = 34\n",
    "matrix = pd.concat([matrix, test], ignore_index=True, sort=False, keys=cols)##数据进行组合\n",
    "matrix.fillna(0, inplace=True) # 34 month\n",
    "# del test##删除test\n",
    "gc.collect()#垃圾回收机制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_cnt_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_block_num  shop_id  item_id  item_cnt_month\n",
       "0               0        2       19             0.0\n",
       "1               0        2       27             1.0\n",
       "2               0        2       28             0.0\n",
       "3               0        2       29             0.0\n",
       "4               0        2       32             0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=red >数据组合</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = pd.merge(matrix, shops, on=['shop_id'], how='left')#数据的组合\n",
    "matrix = pd.merge(matrix, items, on=['item_id'], how='left')#数据的组合\n",
    "matrix = pd.merge(matrix, cats, on=['item_category_id'], how='left')\n",
    "del shops,items,cats\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 13)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.head().shape##做好的一个数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 把所有的分散的数据做成一个文件，那么这里也提供一个思路是否数据预处理可以结束？直接进行模型的训练和预测？"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 真正特征工程\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "##定义滞后特征函数相当于将df里的滞后\n",
    "def lag_feature(df, lags, col):\n",
    "    tmp = df[['date_block_num','shop_id','item_id',col]]\n",
    "    for i in lags:\n",
    "        shifted = tmp.copy()\n",
    "        shifted.columns = ['date_block_num','shop_id','item_id', col+'_lag_'+str(i)]\n",
    "        shifted['date_block_num'] += i##一个一个来\n",
    "        df = pd.merge(df, shifted, on=['date_block_num','shop_id','item_id'], how='left')#组合\n",
    "    return df\n",
    "##定义添加的特征\n",
    "def tianjia1(list_names,list_num):\n",
    "    global matrix#全局化\n",
    "    str1 = list_names[0]\n",
    "    for i in range(1,len(list_names)):\n",
    "        str1 = str1+'_and_'+list_names[i]\n",
    "    str1 = str1+'_avg_item_cnt'##连接符\n",
    "    \n",
    "    group = matrix.groupby(list_names).agg({'item_cnt_month': ['mean']})#相当于重做特征变成均值\n",
    "    group.columns = [str1]\n",
    "    group.reset_index(inplace=True)#重置索引\n",
    "##每新增特征之后就要和原来的进行合并\n",
    "    matrix = pd.merge(matrix, group, on=list_names, how='left')\n",
    "    matrix[str1] = matrix[str1].astype(np.float16)##转换数据类类型\n",
    "    matrix = lag_feature(matrix, list_num, str1)\n",
    "    matrix.drop([str1], axis=1, inplace=True)##和并之后删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8860658, 34)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>shop_city</th>\n",
       "      <th>shop_name1</th>\n",
       "      <th>shop_type</th>\n",
       "      <th>item_category_id</th>\n",
       "      <th>name_1</th>\n",
       "      <th>name_2</th>\n",
       "      <th>...</th>\n",
       "      <th>date_block_num_and_shop_name1_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_type_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_category_id_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_type_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_subtype_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_item_id_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_name_1_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_name_2_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_name_3_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_item_category_id_avg_item_cnt_lag_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>108</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_block_num  shop_id  item_id  item_cnt_month  shop_city  shop_name1  \\\n",
       "0               0        2       19             0.0          0           0   \n",
       "1               0        2       27             1.0          0           0   \n",
       "2               0        2       28             0.0          0           0   \n",
       "\n",
       "   shop_type  item_category_id  name_1  name_2  \\\n",
       "0          5                40      19       4   \n",
       "1          5                19      27      77   \n",
       "2          5                30      28     108   \n",
       "\n",
       "                                  ...                                  \\\n",
       "0                                 ...                                   \n",
       "1                                 ...                                   \n",
       "2                                 ...                                   \n",
       "\n",
       "   date_block_num_and_shop_name1_avg_item_cnt_lag_1  \\\n",
       "0                                               NaN   \n",
       "1                                               NaN   \n",
       "2                                               NaN   \n",
       "\n",
       "   date_block_num_and_shop_type_avg_item_cnt_lag_1  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "\n",
       "   date_block_num_and_item_category_id_avg_item_cnt_lag_1  \\\n",
       "0                                                NaN        \n",
       "1                                                NaN        \n",
       "2                                                NaN        \n",
       "\n",
       "   date_block_num_and_item_type_avg_item_cnt_lag_1  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "\n",
       "   date_block_num_and_item_subtype_avg_item_cnt_lag_1  \\\n",
       "0                                                NaN    \n",
       "1                                                NaN    \n",
       "2                                                NaN    \n",
       "\n",
       "   date_block_num_and_shop_id_and_item_id_avg_item_cnt_lag_1  \\\n",
       "0                                                NaN           \n",
       "1                                                NaN           \n",
       "2                                                NaN           \n",
       "\n",
       "   date_block_num_and_shop_id_and_name_1_avg_item_cnt_lag_1  \\\n",
       "0                                                NaN          \n",
       "1                                                NaN          \n",
       "2                                                NaN          \n",
       "\n",
       "   date_block_num_and_shop_id_and_name_2_avg_item_cnt_lag_1  \\\n",
       "0                                                NaN          \n",
       "1                                                NaN          \n",
       "2                                                NaN          \n",
       "\n",
       "   date_block_num_and_shop_id_and_name_3_avg_item_cnt_lag_1  \\\n",
       "0                                                NaN          \n",
       "1                                                NaN          \n",
       "2                                                NaN          \n",
       "\n",
       "   date_block_num_and_shop_id_and_item_category_id_avg_item_cnt_lag_1  \n",
       "0                                                NaN                   \n",
       "1                                                NaN                   \n",
       "2                                                NaN                   \n",
       "\n",
       "[3 rows x 34 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = lag_feature(matrix, [1,2,3], 'item_cnt_month')#将月销售量分别滞后1，2，3个月\n",
    "tianjia1(['date_block_num'],[1])#将每个月的销售量滞后1个月\n",
    "tianjia1(['date_block_num', 'item_id'],[1,2,3])#将每个月的每个商品的平均销售量分别滞后1，2，3个月\n",
    "tianjia1(['date_block_num', 'shop_id'],[1,2,3])#将每个月的每个商店的平均销售量分别滞后1，2，3个月\n",
    "tianjia1(['date_block_num', 'shop_city'],[1])\n",
    "tianjia1(['date_block_num', 'shop_name1'],[1])\n",
    "tianjia1(['date_block_num', 'shop_type'],[1])\n",
    "tianjia1(['date_block_num', 'item_category_id'],[1])\n",
    "tianjia1(['date_block_num', 'item_type'],[1])\n",
    "tianjia1(['date_block_num', 'item_subtype'],[1])\n",
    "tianjia1(['date_block_num', 'shop_id', 'item_id'],[1])\n",
    "tianjia1(['date_block_num', 'shop_id', 'name_1'],[1])\n",
    "tianjia1(['date_block_num', 'shop_id', 'name_2'],[1])\n",
    "tianjia1(['date_block_num', 'shop_id', 'name_3'],[1])\n",
    "tianjia1(['date_block_num', 'shop_id', 'item_category_id'],[1])\n",
    "\n",
    "print(matrix.shape)\n",
    "matrix.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "##以均值进行聚合\n",
    "group = train.groupby(['item_id']).agg({'item_price': ['mean']})\n",
    "group.columns = ['item_avg_item_price']\n",
    "group.reset_index(inplace=True)\n",
    "\n",
    "##组合之后重新转换数据类型\n",
    "matrix = pd.merge(matrix, group, on=['item_id'], how='left')\n",
    "matrix['item_avg_item_price'] = matrix['item_avg_item_price'].astype(np.float16)\n",
    "\n",
    "##二者均值聚合\n",
    "group = train.groupby(['date_block_num','item_id']).agg({'item_price': ['mean']})\n",
    "group.columns = ['date_item_avg_item_price']\n",
    "group.reset_index(inplace=True)\n",
    "\n",
    "##组合之后再就是重新装换数据类型\n",
    "matrix = pd.merge(matrix, group, on=['date_block_num','item_id'], how='left')\n",
    "matrix['date_item_avg_item_price'] = matrix['date_item_avg_item_price'].astype(np.float16)\n",
    "\n",
    "lags = [1,2,3,4,5,6]##滞后特征函数\n",
    "matrix = lag_feature(matrix, lags, 'date_item_avg_item_price')\n",
    "\n",
    "##重做新特征，用现有的二者相除\n",
    "for i in lags:\n",
    "    matrix['delta_price_lag_'+str(i)] = \\\n",
    "        (matrix['date_item_avg_item_price_lag_'+str(i)] - matrix['item_avg_item_price']) / matrix['item_avg_item_price']\n",
    "##进行一个选择\n",
    "def select_trend(row):\n",
    "    for i in lags:\n",
    "        if row['delta_price_lag_'+str(i)]:\n",
    "            return row['delta_price_lag_'+str(i)] \n",
    "    return 0\n",
    "    \n",
    "matrix['delta_price_lag'] = matrix.apply(select_trend, axis=1)\n",
    "matrix['delta_price_lag'] = matrix['delta_price_lag'].astype(np.float16)\n",
    "matrix['delta_price_lag'].fillna(0, inplace=True)##空值用0来填充\n",
    "\n",
    "##删除这两列所带有的一系列的特征##简便的删除\n",
    "fetures_to_drop = ['item_avg_item_price', 'date_item_avg_item_price']\n",
    "for i in lags:\n",
    "    fetures_to_drop += ['date_item_avg_item_price_lag_'+str(i)]\n",
    "    fetures_to_drop += ['delta_price_lag_'+str(i)]\n",
    "\n",
    "matrix.drop(fetures_to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##迭代循环把负数去掉，重做两个特征\n",
    "ts = time.time()\n",
    "cache = {}##缓存字典\n",
    "matrix['item_shop_last_sale'] = -1\n",
    "matrix['item_shop_last_sale'] = matrix['item_shop_last_sale'].astype(np.int8)\n",
    "for idx, row in matrix.iterrows(): #对整个matrix进行遍历，返回的是索引是索引，row是row\n",
    "    key = str(row.item_id)+' '+str(row.shop_id)#两个相加\n",
    "    if key not in cache:##判断两个相加的不在cache里面\n",
    "        if row.item_cnt_month!=0:#并且item_cnt_month不等于0的时候\n",
    "            cache[key] = row.date_block_num#就把二者相加的放到里面去\n",
    "    else:\n",
    "        last_date_block_num = cache[key]##如果在里面的话就找到“item_shop_last_sale”重新赋一个值\n",
    "        matrix.at[idx, 'item_shop_last_sale'] = row.date_block_num - last_date_block_num\n",
    "        cache[key] = row.date_block_num         \n",
    "\n",
    "cache = {}\n",
    "matrix['item_last_sale'] = -1\n",
    "matrix['item_last_sale'] = matrix['item_last_sale'].astype(np.int8)\n",
    "for idx, row in matrix.iterrows():    \n",
    "    key = row.item_id\n",
    "    if key not in cache:\n",
    "        if row.item_cnt_month!=0:\n",
    "            cache[key] = row.date_block_num\n",
    "    else:\n",
    "        last_date_block_num = cache[key]\n",
    "        if row.date_block_num>last_date_block_num:\n",
    "            matrix.at[idx, 'item_last_sale'] = row.date_block_num - last_date_block_num\n",
    "            cache[key] = row.date_block_num         \n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix['item_first_sale'] = matrix['date_block_num'] - matrix.groupby('item_id')['date_block_num'].transform('min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##空值填充\n",
    "def fill_na(df):\n",
    "    for col in df.columns:\n",
    "        if ('_lag_' in col) & (df[col].isnull().any()):\n",
    "            if ('item_cnt' in col):\n",
    "                df[col].fillna(0, inplace=True)         \n",
    "    return df\n",
    "matrix = matrix[matrix.date_block_num >3]##考虑滞后的参数\n",
    "matrix = fill_na(matrix)\n",
    "matrix = downcast_dtypes(matrix)##数据类型装换函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#添加月,日变量\n",
    "matrix['year']  =  matrix['date_block_num']/12+2013##商+初始年份\n",
    "matrix['month'] = matrix['date_block_num'] % 12##余数就是月份\n",
    "days = pd.Series([31,28,31,30,31,30,31,31,30,31,30,31])#月的天数\n",
    "matrix['days']  = matrix['month'].map(days).astype(np.int8)#转换数据类型\n",
    "matrix['year']  = matrix['year'].astype(np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##添加周变量\n",
    "weekarr = []\n",
    "t = 2\n",
    "count = 0\n",
    "for w in range(3):##三次\n",
    "    for i in [31,28,31,30,31,30,31,31,30,31,30,31]:##12个月的天数\n",
    "        a = [0,0,0,0,0,0,0,count]\n",
    "        count+=1\n",
    "        for j in range(i):\n",
    "            a[t]+=1\n",
    "            if t==6:\n",
    "                t=-1\n",
    "            t+=1\n",
    "        weekarr.append(a)##周数据\n",
    "weekarr = pd.DataFrame(np.vstack(weekarr), columns=['week0','week1','week2','week3','week4','week5','week6','date_block_num'])\n",
    "matrix = pd.merge(matrix, weekarr, on=['date_block_num'], how='left')##和原来要进行组合\n",
    "\n",
    "print(matrix.shape)\n",
    "matrix.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 到此为止数据已经做完啦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix.to_pickle('data2.pkl')\n",
    "del matrix\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test  = pd.read_csv('DATA/test.csv')\n",
    "test.to_pickle('test.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix.to_pickle('data2.pkl')\n",
    "del matrix\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取处理完的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_pickle('data2.pkl')##读取\n",
    "data = data[[\n",
    "    'date_block_num', \n",
    "    'shop_id', \n",
    "    'item_id', \n",
    "    'item_cnt_month', \n",
    "#     'shop_city',\n",
    "    'shop_type', \n",
    "    'item_category_id',\n",
    "#     'item_type', \n",
    "    'name_1',\n",
    "    'name_2',\n",
    "    'name_3',\n",
    "    'item_subtype',\n",
    "    'year',\n",
    "    'month',\n",
    "    'days', \n",
    "    'item_cnt_month_lag_1', \n",
    "    'item_cnt_month_lag_2',\n",
    "    'item_cnt_month_lag_3', \n",
    "    'date_block_num_avg_item_cnt_lag_1',\n",
    "    'date_block_num_and_item_id_avg_item_cnt_lag_1',\n",
    "    'date_block_num_and_item_id_avg_item_cnt_lag_2',\n",
    "    'date_block_num_and_item_id_avg_item_cnt_lag_3',\n",
    "    'date_block_num_and_shop_id_avg_item_cnt_lag_1',\n",
    "    'date_block_num_and_shop_id_avg_item_cnt_lag_2',\n",
    "    'date_block_num_and_shop_id_avg_item_cnt_lag_3',\n",
    "    'date_block_num_and_item_category_id_avg_item_cnt_lag_1',\n",
    "    'date_block_num_and_shop_id_and_item_category_id_avg_item_cnt_lag_1',\n",
    "    'date_block_num_and_shop_id_and_item_id_avg_item_cnt_lag_1',\n",
    "    'delta_price_lag', \n",
    "    'item_shop_last_sale', \n",
    "    'item_last_sale',\n",
    "    'item_first_sale'\n",
    "]]\n",
    "\n",
    "\n",
    "\n",
    "## 数据集的划分\n",
    "#交叉验证集\n",
    "X_valid = data[data.date_block_num == 33].drop(['item_cnt_month'], axis=1)##特征数据集要删掉标签\n",
    "Y_valid = data[data.date_block_num == 33]['item_cnt_month']#标签数据是要保留标签\n",
    "\n",
    "X_zong = data[data.date_block_num < 33].drop(['item_cnt_month'], axis=1)#训练集\n",
    "Y_zong = data[data.date_block_num < 33]['item_cnt_month']\n",
    "\n",
    "# 总得切分一下数据咯（训练集和测试集）\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_valid,Y_valid,test_size = 0.25)\n",
    "\n",
    "X_tests = data[data.date_block_num == 34].drop(['item_cnt_month'], axis=1)\n",
    "\n",
    "xx = pd.concat([X_zong,X_train])\n",
    "yy = pd.concat([Y_zong,y_train])\n",
    "\n",
    "del data,X_valid,Y_valid,X_train,y_train,X_zong,Y_zong\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx.to_pickle('xx.pkl')\n",
    "yy.to_pickle('yy.pkl')\n",
    "\n",
    "X_test.to_pickle('X_test.pkl')\n",
    "y_test.to_pickle('y_test.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模训练预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = time.time()\n",
    "model = XGBRegressor(\n",
    "    max_depth=10,\n",
    "    n_estimators=100,\n",
    "    min_child_weight=0.5, \n",
    "    colsample_bytree=0.9, \n",
    "    subsample=0.8, \n",
    "    num_round = 10000,\n",
    "    nthread = 16,\n",
    "    eta=0.1,\n",
    "    seed=1)\n",
    "\n",
    "model.fit(xx, yy, eval_metric=\"rmse\", eval_set=[(xx,yy),(X_test,y_test)], verbose=True, early_stopping_rounds = 10)\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_tests = model.predict(X_tests).clip(0, 20)##把数据放到0-20之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test  = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv').set_index('ID')\n",
    "submission = pd.DataFrame({\n",
    "    \"ID\": test.index, \n",
    "    \"item_cnt_month\": Y_tests\n",
    "})\n",
    "submission.to_csv('xgb_submission1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.read_csv(\"../input/test.csv\")"
   ]
  }
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